{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capital Bikeshare Project\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1， 导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from sklearn.metrics import r2_score \n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "import matplotlib.pyplot as plt \n",
    "import seaborn as sns \n",
    "\n",
    "#jupyter notenbook的命令，图形出现在Notebook里而不是新窗口\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2， 数据分割\n",
    "将2011年与2012年数据进行分离，其中2011年数据作为训练集， 2012年数据作为测试集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据在程序所在目录下, 根据作业描述最终预测的是2012年每天的单车共享数据，\n",
    "#所以未对小时数据进行分析处理\n",
    "\n",
    "data =pd.read_csv(\"day.csv\")\n",
    "\n",
    "#观察原始数据结构\n",
    "\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#按日期排序, 方便切割检验\n",
    "data = data.sort_values('dteday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#根据说明文件，观察特征\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>726</th>\n",
       "      <td>727</td>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.254167</td>\n",
       "      <td>0.226642</td>\n",
       "      <td>0.652917</td>\n",
       "      <td>0.350133</td>\n",
       "      <td>247</td>\n",
       "      <td>1867</td>\n",
       "      <td>2114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>728</td>\n",
       "      <td>2012-12-28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.255046</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.155471</td>\n",
       "      <td>644</td>\n",
       "      <td>2451</td>\n",
       "      <td>3095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>728</th>\n",
       "      <td>729</td>\n",
       "      <td>2012-12-29</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.253333</td>\n",
       "      <td>0.242400</td>\n",
       "      <td>0.752917</td>\n",
       "      <td>0.124383</td>\n",
       "      <td>159</td>\n",
       "      <td>1182</td>\n",
       "      <td>1341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>730</td>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.255833</td>\n",
       "      <td>0.231700</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>364</td>\n",
       "      <td>1432</td>\n",
       "      <td>1796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>731</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.215833</td>\n",
       "      <td>0.223487</td>\n",
       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>439</td>\n",
       "      <td>2290</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "726      727  2012-12-27       1   1    12        0        4           1   \n",
       "727      728  2012-12-28       1   1    12        0        5           1   \n",
       "728      729  2012-12-29       1   1    12        0        6           0   \n",
       "729      730  2012-12-30       1   1    12        0        0           0   \n",
       "730      731  2012-12-31       1   1    12        0        1           1   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "726           2  0.254167  0.226642  0.652917   0.350133     247        1867   \n",
       "727           2  0.253333  0.255046  0.590000   0.155471     644        2451   \n",
       "728           2  0.253333  0.242400  0.752917   0.124383     159        1182   \n",
       "729           1  0.255833  0.231700  0.483333   0.350754     364        1432   \n",
       "730           2  0.215833  0.223487  0.577500   0.154846     439        2290   \n",
       "\n",
       "      cnt  \n",
       "726  2114  \n",
       "727  3095  \n",
       "728  1341  \n",
       "729  1796  \n",
       "730  2729  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#观察数据头后，再观察一下数据尾\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 数据特征初步筛选及数据分割\n",
    "\n",
    "#### 特征筛选"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'instant', u'dteday', u'season', u'yr', u'mnth', u'holiday',\n",
      "       u'weekday', u'workingday', u'weathersit', u'temp', u'atemp', u'hum',\n",
      "       u'windspeed', u'casual', u'registered', u'cnt'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "0  2011-01-01       1   0     1        0        6           0           2   \n",
       "1  2011-01-02       1   0     1        0        0           0           2   \n",
       "2  2011-01-03       1   0     1        0        1           1           1   \n",
       "3  2011-01-04       1   0     1        0        2           1           1   \n",
       "4  2011-01-05       1   0     1        0        3           1           1   \n",
       "\n",
       "       temp     atemp       hum  windspeed   cnt  \n",
       "0  0.344167  0.363625  0.805833   0.160446   985  \n",
       "1  0.363478  0.353739  0.696087   0.248539   801  \n",
       "2  0.196364  0.189405  0.437273   0.248309  1349  \n",
       "3  0.200000  0.212122  0.590435   0.160296  1562  \n",
       "4  0.226957  0.229270  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 在进行分割前，根据说明文件和任务要求，统一对特征进行筛选\n",
    "####  1，“instant” -- 序列号 对预测结果不影响， 可以删除\n",
    "####  2，“dteday”  -- 具体日期  这个特征的主要作用可以被“season”“month”“holiday”等参数替代， 删除\n",
    "####  3，“yr”     -- 年份参数  分割数据后，在集合中数值不变化，可以分割好数据后 删除\n",
    "####  4，“casual” -- 未注册用户使用单车次数\n",
    "####     “registered” -- 注册用户使用单车次数  “cnt” = “casual”+“registered”,可以在本任务中删除\n",
    "\n",
    "print(data.columns) #观察现在的列参数\n",
    "data=data.drop([\"instant\",\"casual\",\"registered\"],axis = 1) #丢弃部分冗余参数,日期暂不处理，用于分割检验\n",
    "data.head()#观察新的数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dteday  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "0  2011-01-01       1   0     1        0        6           0           2   \n",
       "1  2011-01-02       1   0     1        0        0           0           2   \n",
       "\n",
       "       temp     atemp       hum  windspeed  cnt  \n",
       "0  0.344167  0.363625  0.805833   0.160446  985  \n",
       "1  0.363478  0.353739  0.696087   0.248539  801  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#判断可以直接通过特征“yr”直接对原数据进行分割\n",
    "# yr == 0 --> 2011; yr == 1 --> 2012\n",
    "\n",
    "data_2011 = data[data.yr == 0]\n",
    "data_2012 = data[data.yr == 1]\n",
    "\n",
    "#检验数据分割线, 原数据按日期排序的 head 的日期和 tail中的数据\n",
    "data_2011.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>2011-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.311667</td>\n",
       "      <td>0.318812</td>\n",
       "      <td>0.636667</td>\n",
       "      <td>0.134337</td>\n",
       "      <td>2999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>2011-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.414121</td>\n",
       "      <td>0.615833</td>\n",
       "      <td>0.220154</td>\n",
       "      <td>2485</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dteday  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "363  2011-12-30       1   0    12        0        5           1           1   \n",
       "364  2011-12-31       1   0    12        0        6           0           1   \n",
       "\n",
       "         temp     atemp       hum  windspeed   cnt  \n",
       "363  0.311667  0.318812  0.636667   0.134337  2999  \n",
       "364  0.410000  0.414121  0.615833   0.220154  2485  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2011.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>365</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.370000</td>\n",
       "      <td>0.375621</td>\n",
       "      <td>0.692500</td>\n",
       "      <td>0.192167</td>\n",
       "      <td>2294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>366</th>\n",
       "      <td>2012-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.273043</td>\n",
       "      <td>0.252304</td>\n",
       "      <td>0.381304</td>\n",
       "      <td>0.329665</td>\n",
       "      <td>1951</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dteday  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "365  2012-01-01       1   1     1        0        0           0           1   \n",
       "366  2012-01-02       1   1     1        1        1           0           1   \n",
       "\n",
       "         temp     atemp       hum  windspeed   cnt  \n",
       "365  0.370000  0.375621  0.692500   0.192167  2294  \n",
       "366  0.273043  0.252304  0.381304   0.329665  1951  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2012.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>729</th>\n",
       "      <td>2012-12-30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.255833</td>\n",
       "      <td>0.231700</td>\n",
       "      <td>0.483333</td>\n",
       "      <td>0.350754</td>\n",
       "      <td>1796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>730</th>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.215833</td>\n",
       "      <td>0.223487</td>\n",
       "      <td>0.577500</td>\n",
       "      <td>0.154846</td>\n",
       "      <td>2729</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         dteday  season  yr  mnth  holiday  weekday  workingday  weathersit  \\\n",
       "729  2012-12-30       1   1    12        0        0           0           1   \n",
       "730  2012-12-31       1   1    12        0        1           1           2   \n",
       "\n",
       "         temp     atemp       hum  windspeed   cnt  \n",
       "729  0.255833  0.231700  0.483333   0.350754  1796  \n",
       "730  0.215833  0.223487  0.577500   0.154846  2729  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2012.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#分割结束后、去除dteday\n",
    "data_2011=data_2011.drop([\"dteday\"],axis = 1)\n",
    "data_2012=data_2012.drop([\"dteday\"],axis = 1)\n",
    "data=data.drop([\"dteday\"],axis = 1) #用于统计2011，2012联合数据，及分析correlation table "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.363625</td>\n",
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       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  yr  mnth  holiday  weekday  workingday  weathersit      temp  \\\n",
       "0       1   0     1        0        6           0           2  0.344167   \n",
       "1       1   0     1        0        0           0           2  0.363478   \n",
       "2       1   0     1        0        1           1           1  0.196364   \n",
       "3       1   0     1        0        2           1           1  0.200000   \n",
       "4       1   0     1        0        3           1           1  0.226957   \n",
       "\n",
       "      atemp       hum  windspeed   cnt  \n",
       "0  0.363625  0.805833   0.160446   985  \n",
       "1  0.353739  0.696087   0.248539   801  \n",
       "2  0.189405  0.437273   0.248309  1349  \n",
       "3  0.212122  0.590435   0.160296  1562  \n",
       "4  0.229270  0.436957   0.186900  1600  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#检验结果，看一组即可\n",
    "data_2011.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据分割完成\n",
    "2011年  -- data_2011\n",
    "2012年  -- data_2012"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 数据探索\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 初步观察2011年和2012年数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 365 entries, 0 to 364\n",
      "Data columns (total 12 columns):\n",
      "season        365 non-null int64\n",
      "yr            365 non-null int64\n",
      "mnth          365 non-null int64\n",
      "holiday       365 non-null int64\n",
      "weekday       365 non-null int64\n",
      "workingday    365 non-null int64\n",
      "weathersit    365 non-null int64\n",
      "temp          365 non-null float64\n",
      "atemp         365 non-null float64\n",
      "hum           365 non-null float64\n",
      "windspeed     365 non-null float64\n",
      "cnt           365 non-null int64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 37.1 KB\n"
     ]
    }
   ],
   "source": [
    "data_2011.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 366 entries, 365 to 730\n",
      "Data columns (total 12 columns):\n",
      "season        366 non-null int64\n",
      "yr            366 non-null int64\n",
      "mnth          366 non-null int64\n",
      "holiday       366 non-null int64\n",
      "weekday       366 non-null int64\n",
      "workingday    366 non-null int64\n",
      "weathersit    366 non-null int64\n",
      "temp          366 non-null float64\n",
      "atemp         366 non-null float64\n",
      "hum           366 non-null float64\n",
      "windspeed     366 non-null float64\n",
      "cnt           366 non-null int64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 37.2 KB\n"
     ]
    }
   ],
   "source": [
    "data_2012.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 两年的数据符合基本情况， 2012年的确有366天，闰年。\n",
    "#### 接着看看两年数据的基本统计数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 两年数据的基本统计数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
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       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.498630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>3.008219</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>1.421918</td>\n",
       "      <td>0.486665</td>\n",
       "      <td>0.466835</td>\n",
       "      <td>0.643665</td>\n",
       "      <td>0.191403</td>\n",
       "      <td>3405.761644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.110946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.452584</td>\n",
       "      <td>0.163462</td>\n",
       "      <td>2.006155</td>\n",
       "      <td>0.465181</td>\n",
       "      <td>0.571831</td>\n",
       "      <td>0.189596</td>\n",
       "      <td>0.168836</td>\n",
       "      <td>0.148744</td>\n",
       "      <td>0.076890</td>\n",
       "      <td>1378.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.135583</td>\n",
       "      <td>2132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.647500</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>3740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.656667</td>\n",
       "      <td>0.612379</td>\n",
       "      <td>0.742083</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>4586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.849167</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>6043.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           season     yr        mnth     holiday     weekday  workingday  \\\n",
       "count  365.000000  365.0  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     2.498630    0.0    6.526027    0.027397    3.008219    0.684932   \n",
       "std      1.110946    0.0    3.452584    0.163462    2.006155    0.465181   \n",
       "min      1.000000    0.0    1.000000    0.000000    0.000000    0.000000   \n",
       "25%      2.000000    0.0    4.000000    0.000000    1.000000    0.000000   \n",
       "50%      3.000000    0.0    7.000000    0.000000    3.000000    1.000000   \n",
       "75%      3.000000    0.0   10.000000    0.000000    5.000000    1.000000   \n",
       "max      4.000000    0.0   12.000000    1.000000    6.000000    1.000000   \n",
       "\n",
       "       weathersit        temp       atemp         hum   windspeed          cnt  \n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000   365.000000  \n",
       "mean     1.421918    0.486665    0.466835    0.643665    0.191403  3405.761644  \n",
       "std      0.571831    0.189596    0.168836    0.148744    0.076890  1378.753666  \n",
       "min      1.000000    0.059130    0.079070    0.000000    0.022392   431.000000  \n",
       "25%      1.000000    0.325000    0.321954    0.538333    0.135583  2132.000000  \n",
       "50%      1.000000    0.479167    0.472846    0.647500    0.186900  3740.000000  \n",
       "75%      2.000000    0.656667    0.612379    0.742083    0.235075  4586.000000  \n",
       "max      3.000000    0.849167    0.840896    0.972500    0.507463  6043.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2011.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.0</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
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       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "      <td>366.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.494536</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.513661</td>\n",
       "      <td>0.030055</td>\n",
       "      <td>2.986339</td>\n",
       "      <td>0.683060</td>\n",
       "      <td>1.368852</td>\n",
       "      <td>0.504081</td>\n",
       "      <td>0.481852</td>\n",
       "      <td>0.612166</td>\n",
       "      <td>0.189572</td>\n",
       "      <td>5599.934426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.112185</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.455958</td>\n",
       "      <td>0.170971</td>\n",
       "      <td>2.006108</td>\n",
       "      <td>0.465921</td>\n",
       "      <td>0.516057</td>\n",
       "      <td>0.176112</td>\n",
       "      <td>0.156756</td>\n",
       "      <td>0.134206</td>\n",
       "      <td>0.078194</td>\n",
       "      <td>1788.667868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.107500</td>\n",
       "      <td>0.101658</td>\n",
       "      <td>0.254167</td>\n",
       "      <td>0.046650</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.347708</td>\n",
       "      <td>0.350685</td>\n",
       "      <td>0.508125</td>\n",
       "      <td>0.133721</td>\n",
       "      <td>4369.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.514167</td>\n",
       "      <td>0.497779</td>\n",
       "      <td>0.611875</td>\n",
       "      <td>0.174750</td>\n",
       "      <td>5927.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.653958</td>\n",
       "      <td>0.607646</td>\n",
       "      <td>0.711146</td>\n",
       "      <td>0.231196</td>\n",
       "      <td>7011.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.804913</td>\n",
       "      <td>0.925000</td>\n",
       "      <td>0.441563</td>\n",
       "      <td>8714.000000</td>\n",
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      ],
      "text/plain": [
       "           season     yr        mnth     holiday     weekday  workingday  \\\n",
       "count  366.000000  366.0  366.000000  366.000000  366.000000  366.000000   \n",
       "mean     2.494536    1.0    6.513661    0.030055    2.986339    0.683060   \n",
       "std      1.112185    0.0    3.455958    0.170971    2.006108    0.465921   \n",
       "min      1.000000    1.0    1.000000    0.000000    0.000000    0.000000   \n",
       "25%      2.000000    1.0    4.000000    0.000000    1.000000    0.000000   \n",
       "50%      2.500000    1.0    7.000000    0.000000    3.000000    1.000000   \n",
       "75%      3.000000    1.0    9.750000    0.000000    5.000000    1.000000   \n",
       "max      4.000000    1.0   12.000000    1.000000    6.000000    1.000000   \n",
       "\n",
       "       weathersit        temp       atemp         hum   windspeed          cnt  \n",
       "count  366.000000  366.000000  366.000000  366.000000  366.000000   366.000000  \n",
       "mean     1.368852    0.504081    0.481852    0.612166    0.189572  5599.934426  \n",
       "std      0.516057    0.176112    0.156756    0.134206    0.078194  1788.667868  \n",
       "min      1.000000    0.107500    0.101658    0.254167    0.046650    22.000000  \n",
       "25%      1.000000    0.347708    0.350685    0.508125    0.133721  4369.000000  \n",
       "50%      1.000000    0.514167    0.497779    0.611875    0.174750  5927.000000  \n",
       "75%      2.000000    0.653958    0.607646    0.711146    0.231196  7011.250000  \n",
       "max      3.000000    0.861667    0.804913    0.925000    0.441563  8714.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2012.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "观察发现 \n",
    "1， 2012的cnt明显大于2011年\n",
    "2， weathersit、temp、atemp、humidity也有一定差异"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 仔细观察不同分布图 2011 VS 2012"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Tg4z7EhSWR4D3jTEfWL9vBSYbY+biXHfr1eEC1uUplPIdmuAov2GM4d19x0iKCmVORvxp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\nWAvsAd4A7jLG9A3XJoCI3C0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UT462syQnkaVTkpgQG/6ZusO17Y74lL1ceS/mZyVw6znZ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\nwAmCIAiCoOb4P5E/jn3bVIPPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcd790b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count of total rental bikes\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.distplot(data_2011.cnt.values, bins=50,kde=True)\n",
    "plt.xlabel('Count of total rental bikes (2011)', fontsize=12)\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.distplot(data_2012.cnt.values, bins=50,kde=True)\n",
    "plt.xlabel('Count of total rental bikes (2012)', fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "# 分布差好多，2011的分布好像都有两个高斯分布叠加，2012的负偏态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VmCW/9tRkls0vwfFSVbCcybwLfQew2zn3r0HHIzKXNLbPbA6cUeXRAnoGR4jHXQqjmp2k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\nULHDpUwpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdaf8748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count 值的分布差距比较大， 对Count做Log处理\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.distplot(np.log10(data_2011.cnt.values), bins=50,kde=True)\n",
    "plt.xlabel('Count of total rental bikes (2011)', fontsize=12)\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.distplot(np.log10(data_2012.cnt.values), bins=50,kde=True)\n",
    "plt.xlabel('Count of total rental bikes (2012)', fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dNqmruybOCoq91UU2LCvmL8825i11epbzYh+zbKrLZs7P+w0XUiaza9K7pNKo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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcc6b390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter diagram of total rental bikes \n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.scatter(range(data[data.yr == 0].shape[0]),data[\"cnt\"][data.yr == 0].values,color = \"blue\")\n",
    "plt.title(\"Total rental bikes(2011)\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.scatter(range(data[data.yr == 1].shape[0]),data[\"cnt\"][data.yr == 1].values,color = \"red\")\n",
    "plt.title(\"Total rental bikes(2012)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "# 2012年数据明显大于2011， 但分布相似"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cJup6PKDoCiRwRkSoaBlnttDZZ8vjU7Rs315uxJsUp+kBB9NGqeg3RUar6HctI4EzBbQh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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdb709b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#log后的分布\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.scatter(range(data[data.yr == 0].shape[0]),np.log10(data[\"cnt\"][data.yr == 0].values),color = \"blue\")\n",
    "plt.title(\"Total rental bikes(2011)\")\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.scatter(range(data[data.yr == 1].shape[0]),np.log10(data[\"cnt\"][data.yr == 1].values),color = \"red\")\n",
    "plt.title(\"Total rental bikes(2012)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "# 可以考虑去除2011年的偏离点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MM7Md4oAwM7MsB4SZmWU5IMzMLMsBYWZmWQP+NNdGTkvtbyeddBKLFy/mgQceoK2tjY9/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\nYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5f88128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compare the number of working days in each year\n",
    "sns.countplot(x = \"workingday\", hue=\"yr\", data = data)\n",
    "plt.title(\"Distribution of working days in each year\");\n",
    "\n",
    "plt.show()\n",
    "#符合常理、几乎一样的\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xcc4a780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Compare the weather \n",
    "sns.countplot(x = \"weathersit\", hue=\"yr\", data =data);\n",
    "plt.title(\"Distribution of weather in each year\");\n",
    "\n",
    "plt.show()\n",
    "#2012年天气比2011年好点， 可能是2012的cnt大的一个原因， who knows?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1wZkiTXD8REtXP7XtvSyYob03vmZpdgIt3QNUtugkecp/lTd3A2gPjg8QEWan\nxVLS0MmQQw+cJkuvovITh2vbAXRUvQ9alu38h7BHC/4pP1be3E1ydBgvHbSmyJxezfles9Ni2Fna\nTGVLN3nJWnRxMrQHx08cqe0gNSacFJ3d1+fMmxFLWIhNx+Eov2WMoby5m9ykKKtDUS6zUqMR4ERD\nl9Wh+C1NcPxAR+8AJxu6mJ+hp6d8UViIjYUZcVrwT/mtlu4BuvoGydEEx2dEhYWQHhdBaaMmOJOl\nCY4fePtYI0PGsGCGnp7yVctzEthf1abny5VfOjX+RntwfEt+SjRlzV3arkySJjh+4NXDdUSF2clN\n1sbHVy3NjqdnYIiGjj6rQ1Fqwsqbuwmz20iPi7A6FDVMQUo0A0OGqla9gGEyNMHxcYNDDt44Ws+8\n9FhsOvmdzzpV0VgbIuWPKpq7yUqMxG7TNsaX5LsOak/qaapJ0auofFxReSut3QPMX+wfp6eC9UqI\ngpQYosLsVLX26Dw+yq/0Dzqoaes5XVxO+Y7YiFBSY8J1HM4kaQ+Oj3v7WAN2mzAnLcbqUNQZ2G3C\n4sx4qlq6rQ5FqQmpau3BYXT8ja8qSImmtEnH4UyGJjg+bvPxBlbkJOjkmn5gcVY8NW292hApv3Jq\ngLFeQeWb8lOi6XNNo6EmRhMcH9ba3c++qjYumJNidSjKDUuz4xl0GOo7eq0ORSm3nSrwFxOuIxZ8\nUUGKs8jftpImiyPxP5rg+LB3ipswBj037icWZzkHGlfrQGPlJ7TAn++LjwwlKTqM7SebrQ7F72jK\n7sM2H28gNiKEZdnxHK3tsDocNY6ZKdGEhdiobOnhrDyro1FqfJUtPRMq8GfVRQTBcPHCmf7GguRo\nthxv5M/byk5fTXvT6lxvhea3tAfHRxlj2Hy8kfNnpRBi17fJH9hsQmZ8pPbgKL9RVN4C6ABjX1eQ\nEk3PwBD17VpnayLG/c8pIg+ISL2IHBhjuYjIPSJSLCL7RGSl58MMPo2d/VS19rB2ro6/8SfZiZE6\n0Fj5jaKyFi3w5wfyXeNwTjbp5eIT4U7XwEPAFWdYvh6Y4/rZCPxm6mGp4/XOU1IX6vgbv5KZEKkD\njZXfKCpv1QJ/fiAxKpT4yFAt+DdB4yY4xpi3gTONbroOeNg4bQMSRCTDUwEGq+L6TvKTo/TSTT+T\nnRAJQFWLnqZSvq2nf4jDNe16esoPiIizHk5jF8Zo77C7PDG4IwuoGHa/0vXY+4jIRhEpFJHChoYG\nD+w6MA06HJQ0dOnVU34oKSaM8BCbTtmgfN6+ylYGHUYTHD+RlxxFZ98gzV39VofiNzyR4IzWtzlq\nimmMud8Ys8oYsyo1Vf95j6Vjm7fLAAAgAElEQVS8uZv+IQdrtf6N37GJkJkQqQmO8nlF5a2AFvjz\nF3lJznE4Zc1aLd1dnrhMvBLIGXY/G6j2wHYD3liXBRbXdWIT5yWcwXB5ZKDJSohkW0kT/YMOwkL0\nCjjlm4rKW8hPjtICf34iLS6ciFAbZU1drMzV+e7c4YnW93ngk66rqc4F2owxNR7YbtA6Xt9JTlKU\nTs/gp7ISnQONj9Vp7SLlm4wx7C5v0X+UfsQmQm5SFGVN2oPjLncuE38M2ArME5FKEblNRD4vIp93\nrbIJKAGKgd8DX5y2aINAV98g1a09OrmmH8tyDTQ+UNVmcSRKja6ypYfGzn5W6Mz3fiU/OZr6jj66\n+wetDsUvjNs3aYy5cZzlBrjdYxEFuRMNnRhgTlqs1aGoSUqKDiMi1Mb+qjY2WB2MHxGRB4CrgXpj\nzOJRlgvwS+BKoBu4xRhT5N0oA8OpAn8rcxPYW6GJuL/ITXaOlyrXXhy36AABH3O8vpOIUBtZiZFW\nh6ImySbOisb7tQdnoh5Ca255RVFZC1Fhdual64GUP8lOiMIuQqkmOG7RBMeHGGMoru9kdmrM6flG\nlH/KSozkSE0H/YMOq0PxG1pzy3uKyltZmh2v08D4mbAQG5kJEZQ1a8E/d+in24c0dPTR1jOgp6cC\nQFZCJP1DDh1o7Flac8sDThX40wHG/ikvOZqqlh76BoesDsXnaYLjQ47XdwIwWwcY+71TA431NJVH\nac0tDzhV4E8THP+UlxzFoMPoRQxu0ATHhxTXd5ISE0ZidJjVoagpSooOIy4ihH2V2gh5kNbc8oBT\nBf5W5CZYHImajLxkZ8G/naUtFkfi+zTB8RGDQw5KGjuZraenAoKIsCQ7Xo+yPEtrbnnAqQJ/yTHh\nVoeiJiEmPITk6DAKNcEZlyY4PqKsuZuBIaP1bwLI4qx4jtS267lyN2nNremnBf4CQ35yNLvKmnXi\nzXFojW4fUVzvnJ5hZkq01aEoD1malcDAkOFYbSdLsuOtDsfnac2t6acF/gJDXnIUu8pbONHQpWM2\nz0B7cHzE8foOcpOiCdfpGQLGkixnUrOvqtXiSJRyOlXgb0WOjr/xZ6fG4RSWnqmqgtIeHB/Q2TdI\ndWsv6xamWx2Kzxo56ehNq3MtiuS9zjQZak5SJPGRoToOR/mMUwX+5s/QsX7+LCUmjKToMArLWthw\njm+0hb5Ie3B8wAnX5eE6/iawiAhLsuL1SirlM7TAX2AQEc7KS9QenHHop9wHHK/vJDLUTmaCTs8Q\naJZkx3OsroPeAR1orKzV2TfIweo2zs5PsjoU5QGr8hIpbeqmoaPP6lB8liY4FnNOz9DB7DSdniEQ\nLcmKZ2DIcLRWKxora+0ub8Fh0AQnQKxyvY+7yrQXZyw6Bsdi9R19tPcO6kj4AHVqoPH+qjaW6cBO\nZaGdJ5uxSXAU+DvT2Dhvms44FmfFERZio7C0hSsW65Rso9EeHIvp9AyBLTsxkoSoUPbrOBxlsZ2l\nLSzMjCM2ItTqUJQHhIfYWZ6dwM4yLfg3Fk1wLFZc30FKTDiJUTo9QyA6NdBY56RSVuofdLC7ooVV\neXp6KpCclZ/Iwao2evp1jN9oNMGx0MCQg5ONXXr1VIBbkqUDjZW1Dla30Tvg4JwCTXACydn5iQw6\nDHsqtNbWaDTBsVC5Ts8QFJblJOjsv8pSO12XE6/K1wrGgeTUlBs60Hh0muBY6HhdJ3YRClJ1eoZA\ndqoROlVFVilv21nqnGAzLTbC6lCUByVEhTE3PUZnFh+DJjgWKq7vIDc5ivAQnZ4hkKXGhpObFEVR\nmXYjK+9zOAyFpc2nLytWgeWsvCSKylsYcujEmyNpgmORxs4+qtt69fRUkFiZm0BReYvO/qu8rqSx\nk5buAc7RBCcgnZ2fSEfvIMfqtNbWSFoHxyLvFDcCk7883FfqPEyEJ2P2t79/ZV4iz+2ppqq1h+zE\nKKvDUUFkx0nn6QsdfxOYTl0ZV1jWwoKMOIuj8S3ag2ORt441EBWm0zMEi3+Nw9HTVMq7CkubSYkJ\noyBFx/oFopykSNJiw3VeqlFogmMBh8Pw9rFGZqXq9AzBYv6MWCJD7RRpUS7lZTtKm1mVl4RoWxOQ\nRIRV+YkU6kDj99EExwKHatpp7OxjXnqs1aEoLwmx21iaHa9XUimvqmnrobKlh7O1/k1AW5WXRFVr\nDzVtPVaH4lM0wbHAW8caAJiTrgOMg8nKvEQOVbdrwT/lNTtOOk9b6ADjwHZqfJX24ryXJjgWeOto\nA4uzdE6YYLMy11l1dJ/OS6W85J3iRuIiQliYqYNPA9nCjDiiwuw6DmcETXC8rK1ngF3lLVw0N9Xq\nUJSXnZrFWU9TKW9590QTa2YlY7fp+JtAFmK3sTwngUId4/cemuB42TvFjQw5DBfPS7M6FOVlKTHh\n5CVH6UBj5RXlTd1UtvRw3qwUq0NRXrAqP4nDNe109g1aHYrP0ATHy9462kBsRAgrchKsDkVZYGVu\nIkXlrVrwT027d084a22dPzvZ4kiUN6zKS8RhYLf2EJ+mCY4XGWN461gDa+ekEGLXlz4YrcxNoLGz\nj8oWvdpBTa93TjSRFhvOrFS9mCEYrMhNwCaw86SOwzlF/8t60ZHaDmrbe7l4rp6eClYrdOJN5QXG\nGLaeaOS8Wcla/yZIxEaEsiQrnq0lTVaH4jM0wfGiU5eHXzRPBxgHq/kzYokOs+vlnGpaOWtt9XP+\nbB1/E0zWzEphT0Ur3f06Dgc0wfGqN4/WM39GLOlxEVaHoiwSYrdxVn4S20/qUZaaPqcPpvRqzaBy\n3qxkBoaMHkC56GSbXtLZN0hhaQufWTvT6lCUl4ycEPSm1bkArC5I4mcvHeX+t0uICQ9533KlpurN\now0szIgjTQ+mgsqq/ERC7cK7J5q4UJNb93pwROQKETkqIsUi8vVRlt8iIg0issf18xnPh+rf3j7W\nwKDDcLGengp65850XtVS2thlcSQqELX3DlBU1qJtTRCKCgtheU4CW11X0AW7cXtwRMQO/ApYB1QC\nO0XkeWPMoRGrPmGM+dI0xBgQXj5YS1J0GKvyEq0ORVlsaXY8kaF2TjZ2sTgr3upwVIB5t7iJQYcZ\n8/TUyJ5F5f+Gv6exEaG8ebSe9t4B4oK8Wr47PTjnAMXGmBJjTD/wOHDd9IYVWPoHHbx2pJ7L5qfp\n5eGKULuNVfmJnNQeHDUN3jpWT0x4CCv1YCoozUyNxmFg6wkd5+fOf9ssoGLY/UrXYyN9WET2icjT\nIpLjkegCxPaTTXT0DvKBRTOsDkX5iNUFSdS299KtVUeVBzkchteP1HPB7BRC9WAqKOUmRREdZmfz\n8QarQ7GcO9+A0YoojCzD+ncg3xizFHgV+NOoGxLZKCKFIlLY0BA8L/7LB+uIDLWzdo5esqmcTo3D\nKdFenNN0rN/U7a9qo669j3UL060ORVkkxGZjzaxk3j6m43DcSXAqgeE9MtlA9fAVjDFNxpg+193f\nA2eNtiFjzP3GmFXGmFWpqcExAM7hMLxyqI6L5qYSEWq3OhzlI5blJBAeYqO4vtPqUHzCsLF+64GF\nwI0isnCUVZ8wxix3/fzBq0H6gZcP1WK3CZfO12KiwezCuamUN3cH/YUM7iQ4O4E5IlIgImHABuD5\n4SuISMawu9cChz0Xon/bX9VGbXsvH1ikR1TqX0LtNgpSoilu0ATHRcf6ecArh+o4Oz+RxOgwq0NR\nFlo7x9mBEOynqcZNcIwxg8CXgJdwJi5PGmMOisj3ReRa12p3iMhBEdkL3AHcMl0B+5uXDuoRlRrd\n7LQYmrv6ae7qtzoUX6Bj/aaorKmLY3WdrFuoY/2CXX5yFDlJkbwV5Kep3BqFZozZZIyZa4yZZYz5\nb9dj3zHGPO+6/Q1jzCJjzDJjzCXGmCPTGbQ/eflQHasLkkiI0iMq9V6z05yTIB6v77A4Ep/gsbF+\nEJzj/V4+WAfAB3T8TdATES6ck8rWE430DzqsDscyOsx+Gp1o6KS4vlMbHDWq1Jhw4iNDdRyOk8fG\n+rnWDbrxfi/sq2ZJVjw5SVFWh6J8wKXz0+jqHwrqaWE0wZlGrxxyHlGt08vD1ShEhNlpMZxo6GTI\nMbKzIujoWL8pKGvqYm9lG9csyxh/ZRUUzpuVQniIjdcO11sdimV0Lqpp9I99NSzJiicrIRLQCqLq\n/eamx7KrrIXy5m6rQ7GUMWZQRE6N9bMDD5wa6wcUuk6H3+Ea9zcINKNj/U57YV8NAFctzbQ4EuUr\nIsPsXDA7hVcP1/HdaxYiMtpZ4MCmCc40OdnYxf6qNr511QKrQ1E+bE5aDHYRjtS2Wx2K5Ywxm4BN\nIx77zrDb3wC+4e24/MHf91azKi/x9MGUUgCXLUjntSP1HKvrZN6MWKvD8TpNcKbJ83uqEYGr9YhK\nnUFEqJ2ClGiO1OhAYzU5x+o6OFLbwdVLM3h0e/n7ZqXXnuPAdKb39dSy9p4BAF49XBeUCY6OwZkG\nxhj+treK1QVJzIiPsDoc5ePmzYilobMv6Ityqcl5qrACm8DS7ASrQ1E+Ji4ylKyESF52jQcNNprg\nTIOD1e2UNHRx3fLRyngo9V4LMuIAeO1I8A4GVJMzMOTg2aIq5s+IIyZcO+TV+y3OjGNvRStVrT1W\nh+J1muBMg2eLqgiz21i/WK+eUuNLig4jLTacVw7VWh2K8jOvH6mnqaufVTpzuBrD4qx4AF7cX2Nx\nJN6nCY6H9Q86+OvuStYtStfifsptizLj2XGymYaOvvFXVsrlqcIK0mLDmZMefOMrlHuSY8JZmBHH\nJk1w1FS9driOlu4BPnJWttWhKD+yJCseh4F/HtReHOWeqtYeXj9Sz4fPysZuC75LgJX7rlqaQVF5\nK9VBdppKExwPe7KwghlxEacnO1PKHelx4cxKjWbTvuA7ylKT8/DWUgA+cW6epXEo33flEmcByGDr\nxdEEx4OqW3t461gDN+gRlZogEeGqpZlsP9lEfUev1eEoH9fTP8TjOyr44KIZWvtGjasgJZql2fE8\nW1RldShepQmOB/1lexkAHztbJzlWE3fVkgwcBl7cr6ep1Jk9t6eKtp4Bbj2/wOpQlJ/48MpsDtW0\nc7gmeIqKaoLjIb0DQzy2o4LLFqTrZHdqUuamxzB/RizPFFVaHYryYUMOw+83l7AoM46z8/XqKeWe\na5dlEmoXntkVPO2LJjhT9Oj2ch7dXs63/nqA5q5+bjkv3+qQlJ8SET66Kod9lW0crdXKxmp0m/bX\nUNLQxe2XzA7K+YXU5CRGh3Hp/DSe21PN4JDD6nC8QhMcDzDG8G5JI2mx4Zw3K9nqcJQfu35FFqF2\n4anCCqtDUT7I4TDc93oxs9NiuGKR1tlSE/Phldk0dvYFTVFRLX3pAcUNnVS39vJvy7P0iEpNSVJ0\nGJcvSOevu6v4z/XzCbXrMYj6l5cP1XG0roOPnJXN4ztHT4J17ik1lkvnp5EZH8EjW8v4YBAkyNp6\nesCbRxuIiwhhRa7OBaOm7qOrcmjq6uclrYmjhhkYcvDTl44wMzVa551SkxJit3HT6ly2FDdyoqHT\n6nCmnSY4U1TW1MXJxi7WzkklRI+2lQdcNDeVvOQoHnyn1OpQlA95YmcFJQ1dfP2K+VqGQk3ahnNy\nCbPbeGRrmdWhTDv9jzxFrx2pJyrMztn5SVaHogKEzSbccl4+u8pa2FPRanU4ygd09A7wi1ePcU5+\nEusWplsdjvJjKTHhXLlkBk/vqqSte8DqcKaVJjhTsPl4A8X1nVwyL42wEH0pled8ZFUOseEhPPjO\nSatDUT7gf18+RlNXP/911QId56embOOFs+jsG+RPW0utDmVa6X/lSXI4DD958QiJUaGsLtDeG+VZ\nMeEhfOzsHF7YV0NZU5fV4SgL7a1o5U9bS7n53DyW5ejYGzV1CzPjuGx+Gg+8c5KuvkGrw5k2muBM\n0jNFlRysbmfdwhk69kZNi40XziTEJtzzWrHVoSiL9A86+Maz+0mNCefuD86zOhwVQG6/dDat3QOn\nK/AHIv3PPAnNXf38aNNhzspLZGl2vNXhqACVFhfBzefm8dfdlUFxxYN6v5+/coxDNe388PrFxEWE\nWh2OCiArcxNZOyeFX795ImDH4miCMwk/3nSYjt5BfvRvS7Dp+XA1jT530SzCQ+z8/OVjVoeivGzr\niSZ+9/YJbjwnhw8EQc0S5X3fWL+Atp4B7nvjuNWhTAtNcCbo9SN1PLWrks9eOJN5M2KtDkcFuNTY\ncDZeOJN/7K/h3RONVoejvKS2rZd/f2w3BcnRfPvqhVaHowLUwsw4bliZzZ/eLQvIsX6a4ExAfXsv\ndz+1jwUZcXz5sjlWh6OCxBcunkVOUiTf/dtBBoJkDplg1jswxOf+vIue/kF+e/NZRIVpwXk1fe7+\n4DxC7cK3njuAMcbqcDxKvzluGhhycOcTe+joHeCT5+bxbFGVW8/TsunqlMl+FiJC7Xzn6kV89uFC\nfvvmCf5dk+uANeQw/McTe9hb0conVudSWNpCYWmL1WEpPzey7blpde7p2+lxEXz9ygV8+7kDPFlY\nwcfOzh35dL+lPThuMMbw3ecP8u6JJq5blkVaXITVIakgc/mCNK5emsEvXzuuxf8ClDGGb//tAC8e\nqOWqJRkszNQLGJR3fPycXFYXJPHDFw5T0dxtdTgeowmOG37z1gke3V7OFy6excq8RKvDUUFIRPjv\nf1tCelwEX358N+29gXnVQ7ByOAzf/OsBHt1ezhcvnsX5s1OsDkkFEZtN+NkNyxCBzz2yi57+IatD\n8ghNcMbxu7dO8NN/HuXaZZl89QNah0JZJz4ylF9sWE5VSw9f/HORjscJEL0DQ9z5xB4e21HO7ZfM\n4qta70ZZIDc5il9sWM6hmna+8ew+HA7/H4+jCc4YHA7D//zzCD9+8QjXLMvk5x9dhk0nuFMWOzs/\niR9/aAlbihv5+jP7GQqARiiY1Xf0cuPvt/H83mr+84r5fPWD83UqBmWZS+enc/cH5vLcnmq+/8Ih\nvx90rIOMR9HWPcBXn97Ly4fquGl1Lt+/dpFWK1Y+4yOrcqhq7eEXrx5n0OHg/31kGaH6+fQ7bxyp\n5+6n9tLdP8RvPr6S9UsyrA5JKW6/xFnh+A9bnPPgffvqhX47e70mOCO8fayBrz29j8bOPr5z9UJu\nPT9fj6iUz7nz8rmE2m387KWjNHX288sNy0mOCbc6LOWGps4+frTpCM8UVTJ/Riz33LiCuelaU0v5\nBhHhv65awJAxPPhOKZUtPfxiw3Jiwv0vXfC/iKdJcX0nP/3nEV4+VMfM1Gj++snzWaLTMCgfdvsl\ns0mJCePbfzvIVfds4ccfXsIl89KsDkuNobt/kAffKeV3b52gu3+IL1w8iy9fNoeIULvVoSn1HiLC\nd69ZRF5SFN9/4RBX/OJt/t9HlnHuzGSrQ5uQoE5wjDFsK2nmkW2lvHiglshQO1/94Dxuu6BAGx3l\nFz52di6LMuO54/Hd3PrgTtYtTOfOy+ewSC8x9hkVzd08uqOcx3aU09o9wGXz0/j6+vnM0V4b5eNu\nOb+AxVnx3PXUXjbcv40rl8zgK+vmMTstxurQ3OJWgiMiVwC/BOzAH4wxPxmxPBx4GDgLaAI+Zowp\n9WyontHVN8jWE028dayBN47WU9nSQ2xECF+8eBa3nl9AinbzKz+zOCueF7+8lj9sPslv3jzBK4fq\nOG9WMh9amc26henER/rPJI2B0NYMOQzH6zt462gDLx+qY1dZCzaBDy6awWcvnMnKXC01ofzHqvwk\nXvzyWn73Vgm/31zCpv21rJ2TwkdW5XDJvFRifXgS2HETHBGxA78C1gGVwE4Red4Yc2jYarcBLcaY\n2SKyAfgf4GPTEbA7jDE0d/VT195HbXsPJ+q7OFzbztHaDo7VdTAwZIgKs3PerGS+sm4uVy7J0B4b\n5dfCQ+zcfslsPnFuHn/eVsbjO8u5+6m92ASW5SSwJCueeTNimZceS35KNIlRYT43cNDf2pqBIQe1\nbb1UtHRT2dzDiYZO9la2sr+yjS5XHZH5M2L52hXzuH55FpkJkVaEqdSURYWF8B/r5nLzmjwe3V7O\no9vLueOx3YTahfkz4liWE8/S7ATmpceSER9Bcky4T7Qv7vTgnAMUG2NKAETkceA6YHijcx3wPdft\np4H7RESMB64x27S/htcO12OMYcgYhhwGhzE4HDBkDP2DDrr6BunqH3L+7huko3eQ/hE1QtJiw5k3\nI5ZPX1DAhXNSWZWfSHiIJjUqsMRHhnL7JbP54sWzKCpv5a2j9bx7oolni6ro7Bs8vZ5NICk6nJSY\nMCJC7USE2ogItRMe4vx97bJMLluQ7u3wLWtrOnoH+NGmIww5HAw6DINDzrZm0OFgyGEYGDJ09g3S\n3jNAe+8A7T2D9Ay8txhamN3Ggsw4bjgrm6XZCayZlaxJjQooKTHh3HHZHL50yWyKylt47Ug9e8pb\n+dvuav687V/TQYTYhNTYcKLDQ4gOsxMZZicqLITIMDuhNsFus2G3QVxEKN+axslk3UlwsoCKYfcr\ngdVjrWOMGRSRNiAZeM/0xyKyEdjoutspIkcnE/RklAE7x16cwohYJ+vjntjIv3gsLg/TuCbGrbg8\n/NkZ18kzxHXPxDaV54FwwH/bmtOv43Hg+Wnc0ST56vfiFI1vaiYcnzfamhP/unnG+L7t/iYn3M64\nk+CM1s808mjJnXUwxtwP3O/GPr1KRAqNMausjmMkjWtiNK6J8cG4/LKt8cHX8T00vqnR+KbGyvjc\nqQ5WCeQMu58NVI+1joiEAPFAsycCVEoFDW1rlFIe406CsxOYIyIFIhIGbOD9vbDPA59y3b4BeN0T\n42+UUkFF2xqllMeMe4rKdZ77S8BLOC/dfMAYc1BEvg8UGmOeB/4IPCIixTiPpjZMZ9DTwOdOm7lo\nXBOjcU2MT8Xlx22NT72Oo9D4pkbjmxrL4hM9+FFKKaVUoNEZ+pRSSikVcDTBUUoppVTACaoER0Su\nEJGjIlIsIl8fZXm4iDzhWr5dRPJ9JK6viMghEdknIq+JiKfqjkwprmHr3SAiRkS8cimgO3GJyEdd\nr9lBEXnUF+ISkVwReUNEdrveyyu9ENMDIlIvIgfGWC4ico8r5n0isnK6Y/JHvtp2TCA+S9qQicQ4\nbD2vticTic+KdsXd+KxoX0bs3/faGmNMUPzgHLR4ApgJhAF7gYUj1vki8FvX7Q3AEz4S1yVAlOv2\nF3wlLtd6scDbwDZglS/EBcwBdgOJrvtpPhLX/cAXXLcXAqVeiOtCYCVwYIzlVwIv4qwvcy6wfbpj\n8rcfX207Jhif19uQicboWs+r7ckEX0OvtysTjM/r7cuI/ftcWxNMPTiny8AbY/qBU2Xgh7sO+JPr\n9tPAZSIy3RNqjBuXMeYNY0y36+42nPVBpps7rxfAD4CfAr1eiMnduD4L/MoY0wJgjKn3kbgMEOe6\nHc/7a7x4nDHmbc5cJ+Y64GHjtA1IEJGM6Y7Lz/hq2+F2fBa1IROK0cXb7ckpvtquTCQ+r7cv79m5\nD7Y1wZTgjFYGPmusdYwxg8CpMvBWxzXcbTiz4Ok2blwisgLIMca84IV43I4LmAvMFZF3RGSbOGeo\n9oW4vgd8QkQqgU3Av3shrvFM9PMXjHy17Xjfvl18pQ0Zzlfbk1N8tV05xV/bl+G83ta4M1VDoPBY\nGXgPc3ufIvIJYBVw0bRG5NrdKI+djktEbMD/Abd4IZbh3Hm9QnB2J1+M80h1s4gsNsa0WhzXjcBD\nxpj/FZE1OOu5LDbGOEZ5rrdY8Zn3N77adkx4315uQ96z61Ee84X25HQIozzmC+3KKf7avgzn9e9I\nMPXg+GoZeHfiQkQuB/4LuNYY0zfNMbkTVyywGHhTREpxnlN93gsDA919H/9mjBkwxpwEjuJsmKyO\n6zbgSQBjzFYgAudEdFZy6/MX5Hy17Xjfvl18pQ0ZzlfbE3fjO7WOt9uVicTni+3LcN5va7w5CMnK\nH5zZdwlQwL8GaS0asc7tvHeg4JM+EtcKnAPM5vjS6zVi/TfxziBjd16vK4A/uW6n4OwWTfaBuF4E\nbnHdXoDzyy1eeM3yGXvg31W8d+DfDm99xvzlx1fbjgnG5/U2ZKIxjljfK+3JBF9Dr7crE4zPkvZl\nRAw+1dZ47Q/3hR+co7iPub7o/+V67Ps4j2jAmfE+BRQDO4CZPhLXq0AdsMf187wvxDViXa81SG68\nXgL8HDgE7Ac2+EhcC4F3XI3THuADXojpMaAGGMB5BHUb8Hng88Neq1+5Yt7vzX8q/vTjq23HBOKz\npA2ZSIwj1vVaezKB19CSdmUC8Xm9fRkRn8+1NTpVg1JKKaUCTjCNwVFKKaVUkNAERymllFIBRxMc\npZRSSgUcTXCUUkopFXA0wVFKKaVUwAnYBEdEfisi3/bQtnJFpFNE7K77b4rIZzyxbdf2XhSRT3lq\nexPY7w9FpFFEar2970AnImtF5KjVcajppe2MW/vVdmaaaDtzZn6Z4IhIqYj0iEiHiLSKyLsi8nlX\nuW8AjDGfN8b8wM1tXX6mdYwx5caYGGPMkAdi/56I/HnE9tcbY/401nOmg4jkAHfhnJF2xijLL3bN\naRIQ3HmfPckYs9kYM28yzxWRq0Rki+uzXSsivxeR2GHLw0XkARFpdy3/yrBlYSLytOvvNSJy8Yht\nXyIib4hIm6tirBqDtjNTp+3M9PLhduarInLA9d05KSJfnfQfOQV+meC4XGOMiQXygJ8A/wn80dM7\ncZVdD0R5QJPx7oy408Ib75GXPwfxwA+BTJwVSbOBnw1b/j2cJeLzgEuAr42Y+G8L8AlgtCPmLuAB\nwJIGxw9pOzM12s742D6Gmc52RoBPAok4K0B/SUQ2eDj+8Xmz0qEHKyaWApePeOwcwAEsdt1/CPih\n63YK8ALQinN+mM04k7tHXM/pATqBr+EsNW1wVmEsB94e9liIa3tvAj/GWbG0DfgbkORadjFQOVq8\nON/ofpyVHjuBvcO29276hO0AACAASURBVBnXbRvwLaAMqAceBuJdy07F8SlXbI24KlqO8TrFu57f\n4Nret1zbv9z1NztccTw04nnRI5Z34vwS2ICv46xE2YRz3pOkEbHdirOEeQvOKpZnA/tcr/19w/Zx\nC86qm/e6XsMjwGUjYv8jzsqYVTi/iPYRz/0/1/v5Q2AW8LorrkbgL0CCa/3R3ucx3yfX7e8BTwN/\nBtqBz5zp7x/ltX/P9l3bvtv1WrQBTwARbn7ePwTsH3a/imFVSoEfAI+P8rxK4OIxtnk5UGr1d9mX\nf9B2RtsZbWem1M4MW+ce4F5vf4f9uQfnPYwxO3C+0GtHWXyXa1kqkA580/kUczPOL/A1xtk1/NNh\nz7kIZ1b7wTF2+Ung0zi/kIM438DxYvwn8CPgCdf+lo2y2i2un0uAmUAMcN+IdS4A5gGXAd8RkQVj\n7PJenF/gma6/55PArcaYV4H1QLUrjltGxNk1YnmMMaYauAO43rWtTJyNy69G7HM1zqz/Y8AvcE7u\ndzmwCPioiFw0Yt0SnP8Yvgs8KyJJrmV/wvm6zsY5j84HcH75Rz43DfhvnEcMP+ZfRyM5OBsPxnmf\nz+Q6nI1PAs6GzJ2//0w+ivOfTwGwFPdnTr4QOAggIomufe8dtnwvztdXTTNtZ0al7QzazoxFRATn\n9+XgRJ87VQGT4LhUA0mjPD4AZAB5xjkT7GbjSivP4HvGmC5jTM8Yyx8xxhxwfUm/jfNLZZ986Kd9\nHPi5MabEGNMJfAPYMKLr8v8zxvQYY/bi/NC9rwFzxfIx4BvGmA5jTCnwv8DNU4jtcziP5CqNczbi\n7wE3jIjtB8aYXmPMyzhPhzxmjKk3xlThPKJdMWzdeuAXrvfkCZyz814lIuk4G747Xe9BPc6jqOFd\nnNXGmHuNMYOu16LYGPOKMabPGNOAc86Y4Y3cZGw1xjxnjHG4Pgfu/P1nco8xptoY0wz8HVg+3hNE\nZB3OI+nvuB6Kcf1uG7ZaG87ZmJV3aDvjou2MtjNu+B7OXOPBSTx3SgLtvG8Wzq7EkX6G80V+2ZlM\ncr8x5ifjbKtiAsvLgFA8MzV9pmt7w7cdgvOI8JTh5zy7+deHcbgUnLPOjtxW1hRiywP+KiKOYY8N\njYitbtjtnlHuD4+1asQ/gDKcf38eztezxvV+gfMLMvw1f8/7IyJpOI9u1+L8EtpwHvlMxcjPwJn+\n/io3tjfyfcs808oici7wKHCDMeaY6+FO1+84oHfY7Q439q88Q9uZf9F2RtuZM237Szh79Na6kjWv\nCpgeHBE5G+eXasvIZa4ji7uMMTOBa4CviMhlpxaPscnxjrxyht3OxXn01ojzaCJqWFx2nF3W7m63\nGucHfPi2B3nvF9gdja6YRm7LnS8IjB5nBbDeGJMw7CfCddQ0GVkyrGVxxVft2k8fkDJsP3HGmOHd\noyPj+7HrsaXGmDicg9/kDOuP9z6N9hxP//1jEpEVwPPAp40xr50OyJgWnOMFhh9NL8OC7t9gpO3M\n+2g7o+3MWNv+NM6xRJeZ/5+9e4+Ps6zz///6TM5pzk3SNk3SY3qmZ2k5FwFBRHAVFXFBXBTF4/6+\nruvqup5Wv7uuu56WVb4grIqCKKAcLALlVAq0pefz+ZAmbZM05zSHZpLr98dMMYS0mTQzc89M3s/H\nI4/OZO65552Z9Mrnvu/r4JwnI+XivsAxsxwzuw74HfAb59zWAba5zsymBn/JWwhUw6eHYtYQuHY8\nVH9rZrPMLJPAkvWPuMDwzj1AenAIXgqBDndpfZ5XA0zsO9S0n4eA/8/MJplZFn+9lu4fSrhglt8D\n3zOzbDObAPwfAp3ZQlEDjDaz3D7fuzu4vwkAZlZkZjcMJVc/xcAXzCzFzD5I4Jr2cufcMeBZ4L+C\nn6/PzKb0u67eXzaBo44mMxvP20cJ9f+cB/ucBhLun39AZjYH+AvweefckwNs8mvg62aWb2YzgE8S\n6Ox6+vlpZpYevJtqZumnG/jge5lO4MjVgo+lhvtnSDRqZwamdkbtTPBu/3bmowR+p65yzh0Id/ZQ\nxXOB86SZtRKodv+ZwLXQj59h2wpgBYFfzNeBnznnXgo+9m8EPsQmM/uHIbz+AwQ+7ONAOoGOYTjn\nmoHPAL8gcBRzkkDHw9P+EPy33sw2DLDf+4P7XgkcJHB68PNDyNXX54Ovf4DAEeeDwf0Pyjm3i0Aj\neCD43pQAPyFQ7T8bfO9XE+iEd67WEPhsThDowHejc64++NitBE597yBwCvgRAv0bzuTbwEIC14n/\nDDzW7/G3fM4hfE4DCffPfyZfInCUd58FJn5rM7O+R07fJDDC4jDwMvADF+hYetpuAqfpxwPPBG+f\nPsK+NHh/OYEj2Q4CjbwMTO3M4NTO/JXamYDvAqOBN/rs++4I/AxnZYP3gRMJPzO7jcCQ1Yu9ziIi\niUntzMgWz2dwRERERAakAkdEREQSji5RiYiISMLRGRwRERFJOCpwREREJOGowBEREZGEowJHRERE\nEo4KHBEREUk4KnBEREQk4ajAERERkYSjAkdEREQSjgocERERSTgqcERERCThqMARERGRhKMCR0RE\nRBJOslcvXFhY6CZOnOjVy4tImKxfv/6Ec67I6xxnorZGJP6dSzvjWYEzceJE1q1b59XLi0iYmNlh\nrzOcjdoakfh3Lu2MLlGJiIhIwlGBIyIxwczSzWytmW02s+1m9u0BtrnNzOrMbFPw6xNeZBWR2OfZ\nJSoRkX66gHc659rMLAVYZWZPO+dW99vuYefc5zzIJyJxRAWOiMQE55wD2oJ3U4JfzrtEIhLPdIlK\nRGKGmSWZ2SagFnjOObdmgM0+YGZbzOwRMys7w37uMLN1Zraurq4uoplFJDapwBGRmOGc63HOzQdK\ngfPNbE6/TZ4EJjrn5gIrgF+dYT/3OOcWO+cWFxXF7Ah2EYkgFTgiEnOcc03AS8A1/b5f75zrCt69\nF1gU5WgiEidU4IhITDCzIjPLC97OAK4EdvXbZlyfu9cDO6OXUETiyaCdjM0sHVgJpAW3f8Q5981+\n26QBvyZwNFUPfNg5dyjsaUUkkY0DfmVmSQQOvn7vnHvKzL4DrHPOPQF8wcyuB/xAA3CbZ2lFJKaF\nMooqlKGbtwONzrmpZnYT8H3gwxHIKyIJyjm3BVgwwPe/0ef2V4GvRjOXiMSnQQucEIdu3gB8K3j7\nEeAuM7Pgc0U89+Cayrfcv3lJuUdJRCSR9G1b1K7ElpD64IQwdHM8cATAOecHmoHRA+xHQzdFREQk\n4kIqcEIYumkDPW2A/WjopoiIiETckEZRnWnoJlAFlAGYWTKQS6ADoIiIiEjUDVrghDJ0E3gC+Fjw\n9o3AC+p/IyIiIl4JZRRVKEM37wMeMLN9BM7c3BSxxCIiIiKDCGUUVShDNzuBD4Y3moiIiMi50UzG\nIiIiknBU4IiIiEjCUYEjIiIiCSeUTsYiIiIyTJpRPbp0BkdEREQSjgocERERSTgqcERERCThqMAR\nERGRhKMCR0RERBKOChwRERFJOCpwREREJOGowBEREZGEowJHREREEo4KHBEREUk4WqphBIiX6cHj\nJaeIiMQ+ncERERGRhKMCR0RERBKOChwRiQlmlm5ma81ss5ltN7NvD7BNmpk9bGb7zGyNmU2MflIR\niQcqcEQkVnQB73TOzQPmA9eY2dJ+29wONDrnpgI/Ar4f5YwiEidU4IhITHABbcG7KcEv12+zG4Bf\nBW8/AlxhZhaliCISR1TgiEjMMLMkM9sE1ALPOefW9NtkPHAEwDnnB5qB0QPs5w4zW2dm6+rq6iId\nW0RikAocEYkZzrke59x8oBQ438zm9NtkoLM1/c/y4Jy7xzm32Dm3uKioKBJRRSTGqcARkZjjnGsC\nXgKu6fdQFVAGYGbJQC7QENVwIhIXVOCISEwwsyIzywvezgCuBHb12+wJ4GPB2zcCLzjn3nYGR0RE\nMxmLSKwYB/zKzJIIHHz93jn3lJl9B1jnnHsCuA94wMz2EThzc5N3cUUklqnAEZGY4JzbAiwY4Pvf\n6HO7E/hgNHOJSHzSJSoRERFJOCpwREREJOEMWuCYWZmZvWhmO4PTp39xgG2WmVmzmW0Kfn1joH2J\niIiIREMofXD8wJeccxvMLBtYb2bPOed29NvuFefcdeGPKCIiIjI0g57Bcc4dc85tCN5uBXYSmE1U\nREREJCYNqQ9OcOXeBUD/6dMBLgiuAvy0mc0+w/M1fbqIiIhEXMgFjpllAY8Cf++ca+n38AZgQnAV\n4P8G/jTQPjR9uoiIiERDSPPgmFkKgeLmt865x/o/3rfgcc4tN7OfmVmhc+5E+KKKiEi8enBN5Vvu\n37yk3KMkbxWruWT4QhlFZQRmD93pnPvhGbYZG9wOMzs/uN/6cAYVERERCVUoZ3AuAm4BtprZpuD3\nvgaUAzjn7iawJsydZuYHOoCbtD6MxDIdtYmIJLZBCxzn3CrABtnmLuCucIUSERERGQ7NZCwiIiIJ\nRwWOiIiIJBwVOCIiIpJwVOCIiIhIwlGBIyIiIglHBY6IiIgknJBmMhYRERkJNEdW4tAZHBEREUk4\nKnBEREQk4egSlSQEnVYWEZG+dAZHREREEo4KHBEREUk4KnBExHNmVmZmL5rZTjPbbmZfHGCbZWbW\nbGabgl/f8CKriMQH9cERkVjgB77knNtgZtnAejN7zjm3o992rzjnrvMgn4jEGZ3BERHPOeeOOec2\nBG+3AjuB8d6mEpF4pgJHRGKKmU0EFgBrBnj4AjPbbGZPm9nsqAYTkbiiS1QiEjPMLAt4FPh751xL\nv4c3ABOcc21mdi3wJ6DiDPu5A7gDoLxcUwaIjEQ6gyMiMcHMUggUN791zj3W/3HnXItzri14ezmQ\nYmaFA+3LOXePc26xc25xUVFRRHOLSGxSgSMinjMzA+4DdjrnfniGbcYGt8PMzifQftVHL6WIxBNd\nohKRWHARcAuw1cw2Bb/3NaAcwDl3N3AjcKeZ+YEO4CbnnPMirIjEPhU4IuI559wqwAbZ5i7grugk\nEpF4p0tUIiIiknBU4IiIiEjCUYEjIiIiCUcFjoiIiCQcFTgiIiKScFTgiIiISMJRgSMiIiIJZ9AC\nx8zKzOxFM9tpZtvN7IsDbGNm9lMz22dmW8xsYWTiioiIiAwulIn+/MCXnHMbzCwbWG9mzznndvTZ\n5t0EFr2rAJYAPw/+KyIiIhJ1gxY4zrljwLHg7VYz2wmMB/oWODcAvw5Om77azPLMbFzwuTJMD66p\nfMv9m5dEb3Xkob523+0jnbN/NhERkdOG1AfHzCYCC4A1/R4aDxzpc78q+L3+z7/DzNaZ2bq6urqh\nJRUREREJUcgFjpllAY8Cf++ca+n/8ABPedsieM65e5xzi51zi4uKioaWVERERCREIS22aWYpBIqb\n3zrnHhtgkyqgrM/9UuDo8OOJhMY5x/66k2w72kxVYzsPra2kvCCTm84v45IKFdMiIiPNoAWOmRlw\nH7DTOffDM2z2BPA5M/sdgc7Fzep/I9FS2dDOU1uOUtXYQVqyj9L8DPJHpbL2UAN/3nqMS6cVcVlF\nERmpSV5HFZE4dKKtiz9trGbFzhpaO/1kpydzxYwx/M3Ct/XEkBgSyhmci4BbgK1mtin4va8B5QDO\nubuB5cC1wD6gHfh4+KOKvFVPr2PFzhpW7qkjJyOF980fz8LyPJKTfNy8pJwufw+/WV3J95/exYG6\nNj5+4SQVOSIyJMu3HuNrf9xKU3s3M8ZmU5qfwfGWTr63fCc/fG4P18wey+KJ+QTOBUgsCWUU1SoG\n7mPTdxsHfDZcoUQGc6Kti/tfPcjBEydZPCGfa88bR3rKW4uXtOQkbr94EhMKMvnUA+v5zZrD3H7x\nJHxqiEQkBM/vrOH5XbXMLc3loU/OZea4nDcf21vTyref3MEfN1Wz/0QbH1xUdpY9iRdC6oMjEkv2\n1bbxsfvXUtPSyQcXlbKgPP+s2185awzvW1DCoxuqWXOgngumFEYpqUh88XJKiljzyt46nt9Vy8Ly\nPP5mQSkbK5vYWNn0lm2umTOWjNQknttRQ6+Dj5xfRnKSFgiIFfokJK6sP9zAjXe/Rpe/hzsunTxo\ncXPawvJ8KoqzeGZ7DY3tpyKcUkTi2c5jLTy97Tjnjc/l/QtLSfINfNbXZ8bl04u5ds5YtlU38y+P\nb49yUjkbFTgSN57dfpyb711DXkYKj955IaX5mSE/18x434JAh8Bnth+PVEQRiXPNHd08sr6Kktx0\nPrioNKRL2hdXFHHZtCIeWlvJ7984Muj2Eh0qcCQu/HbNYT79m/XMGJvNI3deyITRo4a8j/zMVJZM\nLmBrVTP1bV0RSCki8cw5xx/WHaGn13HTO8qHdLnpqlljuHhqIV9/fBs7j/WfKk68oD44cShRrpOH\nstSCc44frdjLT5/fy7LpRfzsowvJTD33X9uLpxby+v56Xt5Tx/sXloaULV7fXxEZmo1Hmjhw4iTv\nmz+ewuy0IT3XZ8ZPbprP1T9eyZcf2cwfP3MRKeqP4ym9+xKz/D29fPWxrfz0+b18cFEp9966eFjF\nDUB2egqLJuSzsbKJ5o7uMCUVkXjXfsrP01uPUZafweKJofXt6290Vhr/esMctlW3cM/KA2FOKEOl\nAkdi0il/L596YD2/e+MIn7t8Kv9x49ywHQ1dUlFEj3OsO9wQlv2JSPxbsbOG9lM93DB//LCmknj3\neeO49ryx/GTFXirr28OYUIZKBY7EnJNdfu5bdYAXdtfyrzfM5h+unh7WSbQKRqUyuWgUGw430uve\ntmSaiIwwJ9q6WHuwgXdMKqAkL2PY+/vGdbNJ8hn/9vTOMKSTc6UCR2LK8eZOfvbSPo41d/Lzjy7k\nlgsmRuR1FpXn09jezaH6kxHZvwydmZWZ2YtmttPMtpvZFwfYxszsp2a2z8y2mNlCL7JKYnl2+3GS\nfT6umFEclv2NzU3nzmVTeHrbcVYfqA/LPmXoVOBIzNhW3czdL+/H3+v4xCWTuWbOuIi91uySXNKS\nfWw43Bix15Ah8wNfcs7NBJYCnzWzWf22eTdQEfy6A/h5dCNKojnS0M62oy1cUlFIdnpK2PZ7x6WT\nGZ+Xwf9dvhOnM8WeUIEjnuvpdTy3o4YH11YyJieNzy6bSnlB6HPcnIvUZB/njc9la3Uzp/y9EX0t\nCY1z7phzbkPwdiuwE+i/muENwK9dwGogz8wiVwlL1Dy4pvLNr2hasbOGzNQkLq4I7wzn6SlJfPHK\nCrZUNfP8ztqw7ltCowJHPFXf1sW9rxzgxd21LCzP5xOXTCYnI3xHUWcztzSP7h7HvtrWqLyehM7M\nJgILgDX9HhoP9J1JrYq3F0GY2R1mts7M1tXV1UUqpsS5Nw41sLe2jcumFZGWHP6FeN+/YDwTRmfy\nw+f20NurszjRpgJHPOGc442DDfz3C/uobe3kw4vLuHFRaVTnjZhUOIqMlCS2H9WkXLHEzLKAR4G/\nd871/3AG6m3+tr8czrl7nHOLnXOLi4qKIhFTEsCPnttDVloySyaNjsj+k5N8fPGKCnYca+HZHZpB\nPdpU4EjU7TzWws33ruGPm6opK8jgC++sYF5ZXtRzJPmMGWOz2XW8FX+PLlPFAjNLIVDc/NY599gA\nm1QBfZdtLgWORiObJJbVB+p5bX89l00rIjU5cn8Kb5g/nomjM/n5ywfUFyfKNJNxAorkNezh7Hvn\nsRZ+/tJ+ntpylJyMFK6fV8L5kwrOOOfEcGZsDjXnrJIcNh5pYu3BBi6cqlXGvWSBuQDuA3Y65354\nhs2eAD5nZr8DlgDNzrlj0cooicE5xw+f20NxdhrnTyqI6Gsl+YzbL5nMv/xpG+dPLGBS4dCXmZFz\nowJHIqq6qYPnd9bwx43VbKxsYlRqEp+8dDJ3XjaF5Vu9P2VbUZxNSpLxzPbjKnC8dxFwC7DVzDYF\nv/c1oBzAOXc3sBy4FtgHtAMf9yCnxLnX99ez9mAD33rvrKhcFr9xYSk/em4Pr+ytU4ETRSpw5Jw5\n5+js7qW1s5vWLn/g304/rZ1+mjq6+fnL+zjS0AHA1OIsvv6emdy4qJS8zFSPk/9VarKPqUVZvLi7\njm97HWaEc86tYuA+Nn23ccBno5NIEpFzjv96bg9jc9K56fxyHttQHfHXzEhN4palE/jJ83upbe2k\nODs94q8pKnBGnK7uHhpOnsLf20tPr6PXgc8CC8VZ8N+eXkdrZzfNHX52H2+l7S3FSzctnX+97x9g\nZEBKkpGdnsLSyQV87IKJXD6jmClFWR78tKGpGJPNE5uPcrj+5DmtUi4i8WPFzlrWH27ke38zh/SU\n8I+cOpNbLpjAXS/sY83BBt47tyRqrzuSqcBJcPVtXWypbmZvTSs1LV10dPfw7ad2nNO+0pJ9ZKen\nkJ2eTFlBJjnB24GvwO2c9BTSkn2YWdyswl1RHCi+Vu49wS0qcEQSVk+v4wfP7GJS4Sg+tLhs8CeE\nUWFWGnPG57DhcCNXzxob0Y7NEqACJ0E1tZ/iL9uPs7WqGQeMz8tgbmku+ZmpXDh1NEk+Iyl41sY5\n6HXQ6xzOOcyMnIxAwbJmfz2j0gIFTKL+hywYlUpZQQav7KnjlqUTvI4jIhHy2IYq9tS08T83L4zq\nlBSnLZ08ms1VzWw+0sQ7Ity5WVTgJKRt1c08trGK3l64pKKQC6cUvmXyvKGcWTnW1BmJiDHFzLik\noognNx2lu6fXk4ZPJB70HZ3Yvx0ZaOTiKX8vhxtOUt3YQa9zpCUnUV6QGZYFLc/22gO1cZ3dPfzo\nuT3MLc3l2vPGhvX1Q1VekMnYnHRWH6xn8cT8YY0UlcGpwEkwD7x+iAfXVjI+L4Ob3lHG6Kw0ryPF\nhUsrCnlwTSWbjzSxeKKOrESGo+NUD6v21fHq/voBl0LJTkum4eQpbrtwIrmZ0Zm5/DerD3O0uZMf\nfHAedoapKSLNzDh/UgFPbD7K0eZOxoe50JO3UoGTQB5aW8m/PL6dGWOzuXlJOck+nYkI1QVTCvEZ\nvLL3hAockWE40tDOg2srae7oZs74XBZPyKe8IJPUZB9tnX4O1Z9kQ2UjP1qxh1+9foivXDM94v1h\nWjq7uevFfVxSUchFHk8HMa80j+Vbj7H+cKMKnAhTgZMg3jjUwNf/tI3LphVxxYxiFTdDlJuRwuyS\nXNYcrPc6ikjc2lLVxB/WV5GTnsxnlk2hNP+ti+bmZKQwtzSPuaV5zC/L4xuPb+Mrj27lme01LJ08\nmqy0yPxJ+uGze2ju6OYr18yIyP6HIiM1iVklOWw+0sS1c8aSrEviEaN3NgE0njzFFx7aSGl+Bv99\n8wL9hzlHSyYVsLGyiS5/j9dRROLOruMt/H7dEUrzM/js5VPfVtz0N6skhz98+gK++d5ZrNp7grte\n2MuRhvaw59pS1cSvXj/ELUsnMGd8btj3fy4WlefT0d3DzuNa6DeS9JcwAdx6/1pqW7u4bm4JT23W\nrPXn6vxJBXT5e9l8pNnrKCJxZVt1Mw+uqWRsbjofu2AimamhnYkxMz5+0ST++NkLSfIZ97xygPWH\nG8KWq7unl6/9cStFWWn8w9XTw7bf4ZpSnEVuRgobDjd6HSWhqcCJc/tq29ha3czl04t0PXeYzp9U\ngBmsOaDLVCKhauvy87kHNzAqLZnbLpx0TpPnzS7J5bPLpjJp9Cge3VDNNx/fRncYFsD9z2d3s626\nhe/cMJuc9Oh0Zg6Fz4x5pbnsrW3lZJff6zgJSwVOHPP39vLk5qMUjErlkooir+PEvbzMVKaPyWbN\nwfAdQYokum/8aRuVDe18aHHZsPrQZKYl87ELJ3Lx1EJ+9fphPvqLNZxo6zrn/b28p47/9/IBbl5S\nzjVzxp3zfiJlXlkevQ62HdUZ40gZtMAxs/vNrNbMtp3h8WVm1mxmm4Jf3wh/TBnI2oMN1LV1cd3c\ncZq7JUyWTh7N+sON9AywBIWIvNXLe+p4bGM1n39nRVgWkUzyGdeeN44ff3g+m480cf1/r2Jb9dAL\ngJ3HWvj8gxuYPiabb1w3a9i5ImFsTjpF2Wm6JB5BofxV/CVwzSDbvOKcmx/8+s7wY8lgunt6Wbmn\njomjM5k+JtvrOAljyaQCOrp7qG7q8DqKSEzr7O7hm49vY3LhKD5z+ZSw7vt9C8bz6J0XAvCBn7/G\nI+urCKyzOrgTrV3cev9aMlOTue+2xVFdb2oozIx5pXkcqj9JU/spr+MkpEHPJzrnVprZxMhHkaFY\nd7iRlk4/Ny4q82zSqkS0aEI+AJUN7ZQXnH0UiEiiG2h24tPuXXmAQ/Xt/Ob2JaQlh7+ImDM+lyc+\nfzGfe3AD//CHzdy78gDvnVdCwajUM874u/NYC39Yf4TM1GQevGPJoCO5vDavNJcVO2vYWt08YDcD\nzXQ8POG6rnGBmW02s6fNbPaZNjKzO8xsnZmtq6urC9NLjzz+3l5e3l3LhNGZTCnS4pDhVJyTzvi8\nDCojMFxVJFG0d/n5fysPcM3ssVxcEbmJ8wqz0vjtJ5bynvPGceBEGz98bjePrD/Cqr0n3pzOobO7\nh4MnTvLA6sM8sPowBZmpPP7Zi6iIgzPbo7PSGJebzo5jLV5HSUjhmFVpAzDBOddmZtcCfwIqBtrQ\nOXcPcA/A4sWL1cnhHG2rbqal08/7F5bq7E0ELJyQz8o9KsBFzmTl3hOcPOXn/7xrWsRfK8lnXDS1\nkDnjc1m1t461hxr42/vW4DPISkum/VQP/l5HeoqPK2YUc+m0Isri6OzrzHE5vLirltbObq+jJJxh\nFzjOuZY+t5eb2c/MrNA5d2K4+5aBvba/nsKsVKYWZ3kdJSEtKMvjyc1Hae7oJjcjdoaWisSC1s5u\nXj9wghvmlTAtimdJcjNSeM/cEq6aNZayggw2H2mipdNPZmoSTe3dTC3Oitn+NmczuySHF3bVskuT\n/oXdsAscMxsL1DjnnJmdT+CylyYSiZAjDe1UNXbw3rnj8OnsTUQs7NMP57wYmflUJFa8uq8ef4/j\ni1dG/uzNQFKTaq3BWgAAIABJREFUfVwxcwxXzBzz5vfO1lco1o3NSSc/M4UdR3WZKtwGLXDM7CFg\nGVBoZlXAN4EUAOfc3cCNwJ1m5gc6gJtcqN3dZcheP1BPWrKPheX5XkdJWLPG5ZDsM46owBF5iy5/\nD2sP1TN7fG5YhoVLYDTVrHE5rD7YQFuXP2LrcY1EoYyi+sggj98F3BW2RHJGHad62FbdzKIJ+aTF\nwanYeBoB0D9riToai7zN+sONdHb3crHHK3Inmlkluby6v56Xdtdy3dwSr+MkDM0OF0e2VDfh73Us\nnlDgdZSEV16QydGmDvy9w58uXiQR9DrHa/vrKS/I1BQKYTZhdCajUpN4ZnuN11ESigqcOLL+cCNj\nc9IpyUv3OkrCKy/IxN/rONbU6XUUkZiw53grDSdPcZHO3oSdz+zN0VSnh7/L8KnAiRM1LZ1UNXaw\ncEK+hoZHwelhprpMJRLwxqEGstKSmTUux+soCWnWuBzauvy8vl9jdMJFvZnixMbKRnwG88vyvI4y\nIuRmpJCbkaICRwRo6ehmd00rF08tIskXOMAa7sil4Tx/sOeerf9frI64mlKcxajUJJ7dUcOy6cVe\nx0kIOoMTB5xzbKlqpqI4Wz3so6i8IJMjKnCiRgv7xq71lY30OnjHRI3ejJSUJB/LphezYkdNyOtu\nydmpwIkDRxraaeroZm6phixHU3lBJk0d3dS0qB9OlPwSLewbc5xzrDvUwOTCUYzOSvM6TkJbNr2I\n2tYuLd0QJipw4sDm6maSfYFOaBI9p/vhbKxs9DjJyOCcWwk0eJ1D3qqyoZ3G9u43J8CUyLlsemDB\nzZd2a6mYcFCBE+N6eh3bqpuZPjY7Lqchj2cluekk+YwNlU1eR5G/0sK+UbbpSBMpScZsHWBFXHF2\nOrNLcnhZBU5YqMCJcW8caqC1068ZdT2QnOSjJDddZ3Bix+mFfecB/01gYd8BOefucc4tds4tLioq\nilrAROPv7WVrdTMzxubExeSiieCyaUWsr2ykuUOLbw6XeqzGuL9sO06yz5gxNjJHT7E6oiBWlBVk\nsrGyCX9PL8lJOh7wkhb2jb69NW20n+rR6M0oWja9mJ+9tJ/X9unXerjUYscw5xzPbD9OxZhsUpP1\nUXmhLD+Tju4e9tS0eR1lxDOzsRacBEoL+0bHlqomMlKSqBiT5XWUEWNheR7Z6cnqhxMG+qsZw7ZU\nNXOsuZPZJbr27ZXS/Awg0A9BIiu4sO/rwHQzqzKz283s02b26eAmNwLbzGwz8FO0sG9E+Xt72XW8\nNbj4rP5UREtyko9LKgp5eU+dhosPky5RxbBnth8nyWfMGJvtdZQRq2BUKvmZKWw+0hTTi4UmAi3s\nG1sO1J2ky9/LLB1gRd2yacUs33qc4y2djMvN8DpO3FJZHsP+sv04F0weTWaq6lCvmBnzyvJ0BkdG\nnO1HW0hN8jG1WJenou3SaYGO8bo0PjwqcGLUgbo2DtSd5F2zx3gdZcSbV5rHntpW2rr8XkcRiYpe\n59h5rIVpY7JIUef6qBubm86MsdnsqWn1Okpc029ujHphVy0Al2tNEs/NL8/DOdha1ex1FJGoONLQ\nTluXn1klmp7CK8umF3O4/iSd3Vpd/FypwIlRL+6upaI4683ZdMU780oDQ2Q3V+kylYwMO462kGTq\n/+elZdOL6HWwv06Xqc6VCpwY1NblZ+3BBt45Q2dvYkHBqFQmjM5kk2Y0lhHAOcf2Yy1MLhql2dM9\ntLA8n9RkH/tqVeCcKxU4MWjV3hN09zguV4ETM+aV5ukMjowINS1dNJw8pdFTHktN9jG5cBR7VeCc\nMxU4MejFXbVkpyezSIvbxYz5ZXkca+7UyuKS8HYca8aAWVp7ynNTi7NoOHmKhpOnvI4Sl1TgxBjn\nHC/uruXSiiKNXogh84JT1Wu4uCS6HUdbKCvIJDs9xesoI15FcaAP1N5ajaY6F/oLGmO2H22htrVL\nl6dizOySHJJ9pgJHElpT+ymOavb0mFGYlUpeRor64ZwjFTgx5oVdtZgFetBL7EhPSWLmuBw2q8CR\nBLY7OO/KdI2eiglmxtTiLPbXtdHTq2UbhkoFTox5cXctc0vzKMxK8zqK9DO/LI8tVc1qaCRh7alp\nIz8zhSK1PzFjanEWnd29VDd1eB0l7mgNgBhS39bFpiNN/P0V07yOIgOYV5bHA6sPc6CujYoxOsKV\nxNLl72F/bRsLyvMILtouQ/Tgmsqw73NqURaG+uGcC53BiSGB1WPh8hm6PBWL5gc7Gm/UZSpJQOsO\nNXKqp5fpKt5jSmZaMiV5GezTulRDpgInhrywq5bCrDTmaHr0mDS5cBTZ6cnqhyMJ6aXdtST5jMlF\nWlwz1lQUZ3GksZ3Wzm6vo8QVFTgxwt/Ty8o9dVw+vQifT6eHY5HPZ5rwTxLWi7vrmFQ4itRk/VmI\nNVPHZNHr4PX99V5HiSuD/iab2f1mVmtm287wuJnZT81sn5ltMbOF4Y+Z+NYfbqSl06/lGWLcvLJc\ndh1r1QJ4klCqGtvZV9vGNF2eiknlBZmkJvl4Ze8Jr6PElVBK9V8C15zl8XcDFcGvO4CfDz/WyPPi\n7jqSfcbFFYVeR5GzmFeah7/Xsf2oVhaXxPHS7joA9b+JUck+H5MKR7FqnwqcoRh0FJVzbqWZTTzL\nJjcAv3bOOWC1meWZ2Tjn3LEwZUxI/Xvbv7irlndMLIjK7KGR6Okfy68bTm92NK5sYtGEAo/TiITH\nS7trKSvIoDAr1esocgYVY7J4assxjjS0U1aQ6XWcuBCOi63jgSN97lcFv/c2ZnaHma0zs3V1dXVh\neOnE0NR+it01rbo8FQeKc9IpyU1nc5XO4Ehi6PL38Nr+epZNK9bw8Bg2Ndj5W5epQheOAmeg/xED\nzoTmnLvHObfYObe4qEhDoU/bdTwwv4GWZ4gP88vz2HSk0esYImHxxsFG2k/1aHqKGFeUnca43HRW\n7dPJgVCFo8CpAsr63C8FjoZhvyPG7uOtlBdkMqVolNdRJATzSvM40tBBfVuX11FEhu2l3bWkJvu4\nYLL6/8UyM+OSikJW7T2h2dRDFI6ZjJ8APmdmvwOWAM3qfxO67p5eDpxo46NLJuj0cJw43Q9nc1UT\n75wxxuM0IsPz0p46lkwqICM1yesob0qE/nrnYrCf28xo6fTzn8/s5ivvnhGlVPErlGHiDwGvA9PN\nrMrMbjezT5vZp4ObLAcOAPuAe4HPRCxtAjpQd5LuHqfFNePInPG5+Aw2HVE/HIlvp4eHXzZN7U88\n+OuyDZrVOBShjKL6yCCPO+CzYUs0wuyuaSElyVg6ebTXUSREo9KSmTYmm02a0TiszOx+4Dqg1jk3\nZ4DHDfgJcC3QDtzmnNsQ3ZSJZeWeQIdVHWDFh1Gnl23QulQh0ZSVHnLOsft4K1OKskhPiZ3TwzK4\n+WV5bD7SRKC+lzD5JZpzK6pe3lPL+LwMpmh5hrgxtTiLyoZ22rr8XkeJeSpwPFTb2kVjezfTx2py\nrXgzvyyP5o5uDtW3ex0lYTjnVgINZ9nkzTm3nHOrgTwzGxeddInnlL+XV/fVc9n0IvX/iyNTiwPL\nNqzWsg2DUoHjod3B4eGaPTT+zDvd0ViXqaIp5Dm3ZHAbKhtp6/Kr/02cmVCQSUqS8cpeDRcfjAoc\nD+2uaWVsTjp5mZo9NN5MG5NNZmqS+uFEV8hzbmlS0cG9vCewPMyFU9T/L54kJwWWbdCEf4MLxzBx\nOQed3T0crj/JJRWBo6eROixyKGLlPTqdY0xOOs/tqOFb18/2ONGIEfKcW865e4B7ABYvXqyOUgN4\naXcdiyfmR2V5GAmviuJs/rz1GFWN7ZTma9mGM9EZHI/srW2j1+nyVDybMDqTY80dnFRnv2h5ArjV\nApaiObfOWU1LJzuPtXDZNM2eHo+mFgc6ha/SWZyzUoHjkd3HW8hISdKiaXFsQsEoep364YSL5tyK\nnpV7Apft1P8mPhVnpzEmJ02XqQahS1Qe6Ol17DreyrQxWST5NHohXpUXZGLAG4cauXCqprkfLs25\nFT0v76mjODuNmeNGxhnkWLm8HS6BZRuKWLGzhp5ep78jZ6AzOB7YUBlY3G7muByvo8gwZKQmMSYn\nnXWHzzayWSS2+Ht6eWXvCS6bpuHh8eySikKa2rvZVq0Z1c9EBY4HVuyoIcmMaep/E/fKR2eysbJJ\ni99J3Nhc1UxzRzeXafbiuHZR8KyxhoufmQocDzy3o4bJRaM0e3ECmFCQSVuX/805jURi3ct76vAZ\nXKzLqnGtMCuN2SU56odzFipwomx/XRsHTpxkhi5PJYSJo0cB8MYhXaaS+PDy7loWlOdr/q0EcElF\n0ZsTNsrbqcCJsud21AAwU8szJIS8zBRKctNZc1DTpkvsq2/rYkt1s0ZPJYhLKgrp7nGsOaD2ZyAq\ncKJsxY4aZpfk6OgpQZgFVoJfe7BBC29KzFu17wTOaXh4olg0IZ/0FJ8uU52BCpwoOtHWxfrKRq6a\nNcbrKBJGSyYXcKLtFPvr2ryOInJWL++uo2BUKueNz/U6ioRBekoSSyaNVkfjM9A8OFH0wq5anIMr\nZ45hS5WG9sWLwebQWDIpsJbP6gMNTC3WpUeJTf6eXl7cXctl04rwad6UhHFJRSHf/fNOjjZ1UJKX\n4XWcmKIzOFH03I4aSnLTmV2iDsaJZMLoTMbmpLNa18Elhq0/3Ehjezfvmj3W6ygSRqfXM9SyDW+n\nMzhR0tndwyt76/jQ4jJNrpVgzIwlkwt4bX89zjl9vhKTnt1RQ2qSj0v79L9JtBl+R6JpY7Iozk7j\n5b11fOgdZYM/YQTRGZwoeWl3HZ3dvbxrlo6eEtHSyaOpa+1if91Jr6OIvI1zjud21HDR1NFkpem4\nNpGYGZdNK+KVPXV09/R6HSemqMCJkqe2HGX0qFSWTi7wOopEwOlJ01aps5/EoN01rVQ2tHOVDrAS\n0lWzxtDS6WftQc3H1ZcKnChoP+Xn+Z21XDNnLMlJessTUVlBJhNGZ7Jqn66DS+x5bnsNZnDlrGKv\no0gEXFxRSFqy78151iRAf22j4IVdtXR093Dd3BKvo0gEXTS1kNUHGnSaWGLOsztqmF+WR3F2utdR\nJAIyU5O5pKKQ53bUaD6uPlTgRMFTm49RlJ3G+ZN0eSqRXTK1kLYuP5uONHkdReRNx5o72FrdrP5/\nCe7KmWOobupg5zGti3eaCpwIa+vy8+LuWt5z3jiSNPdEQrtwSiE+Q7OKSkxZEbxsoQlGE9sVM8dg\nhi5T9aECJ8JW7Kihy9/LdXPHeR1FIiw3M4XzSvPU0VhiyrM7aphcOIqpxVleR5EIKspOY0FZHit2\nqsA5TQVOhDy4ppIH11Ry98v7yc1IYWF5vteRJAoum1bEpiNNNJw85XUUEZo7unl9fz1XzdbZm5Hg\nyllj2FrdzLHmDq+jxAQVOBHUcaqHvTVtnDc+V1OjjxBXzCim18HLe2q9jiLC8ztr8Pc69b8ZId4V\nvAy5QpepAM1kHFE7jrXQ45wWthtBzhufS2FWGs/vrOVvFpR6HUdGuCc3H2V8XgYLyvIAzVycSAb6\nLJ1zjB6Vyq9fP8wtF0yMfqgYE9IZHDO7xsx2m9k+M/unAR6/zczqzGxT8OsT4Y8afzZXNZGfmUJp\nvhZAGyl8PuOdM4p4WbOKiscaT57ilb0nuG7eOJ1BHiHMjJnjcjhQd5LWzm6v43hu0ALHzJKA/wHe\nDcwCPmJmswbY9GHn3Pzg1y/CnDPuNLWfYn9tGwvK87U20QjzzhljaO30s+5Qo9dRZARbvu0Y/l7H\n9fM0/9ZIMmtcDj3O8cIuXSYP5QzO+cA+59wB59wp4HfADZGNFf82HmnCgToXj0AXVxSSmuTTaAbx\n1BObjjKlaBSzxuV4HUWiqHx0JjnpyTy15ZjXUTwXSoEzHjjS535V8Hv9fcDMtpjZI2Y24JKmZnaH\nma0zs3V1dYk7lNY5x4bDjUwqHEXBqFSv40iUZaUlc3FFIX/ZdlyzioonqhrbWXuogevnjdcZ5BHG\nZ8ac8bm8vLtuxF+mCqXAGeh/R/9W+0lgonNuLrAC+NVAO3LO3eOcW+ycW1xUVDS0pHFk3eFG6k+e\nYpHO3oxY1543juqmDs1qPATq6xc+j66vBuADiwY6FpVEN3d8Lqd6ekf8pH+hFDhVQN8zMqXA0b4b\nOOfqnXNdwbv3AovCEy8+PbS2krRkH7PH69TwSHXVrDGkJBlPbzvudZS4oL5+4dPb6/jD+iNcNKWQ\n0vxMr+OIB0oLMinJTR/xl6lCKXDeACrMbJKZpQI3AU/03cDM+k7Tez2wM3wR40tT+yme2nKM+WV5\npCUneR1HPJKbkcLFUwv585ZjukwVGvX1C5PVB+qpauzgg4s1TcFI5TPjunklvLK3jsYRPOnooAWO\nc84PfA54hkDh8nvn3HYz+46ZXR/c7Atmtt3MNgNfAG6LVOBY98j6Kk75e7Wwprx5mWpzVbPXUeJB\n2Pr6wcjp7zeQh9cdITs9matna3K/kex988fT3eN4csvRwTdOUCFN9OecWw4s7/e9b/S5/VXgq+GN\nFn+cczy4ppJFE/IZl/vWuW80wVbi6v/Z3rykHIB3zR7LP/9pG3/cUMX84ERrckah9vV7yDnXZWaf\nJtDX750D7cw5dw9wD8DixYtHzCm0utYulm89xs3nl5OeojPII9mskhxmjsvh0Q3V3DpCJ/3TUg1h\n9MreExw4cZKPBv/AyciWm5HC1bPH8vjmo3T5e7yOE+vU1y8MHlpbSXeP49YLJ3odRWLABxaOZ/OR\nJvbVtnkdxRMqcMLovlUHKcpO4z1aOVyCblxUSlN7Ny/s1KRbg1Bfv2Hq7unlt2sOc0lFIVOKtHK4\nwPXzS0jyGY9tqPI6iidU4ITJ3ppWXt5Tx61LJ6hzsbzp4qmFjMlJ45H1I7OBCZX6+g3fX7Ydp6al\ni9t09kaCirPTuWxaEY+srxqRS8eowAmT+189SFqyj48uneB1FIkhST7j/QtLeWlPHcebO72OE9Oc\nc8udc9Occ1Occ98Lfu8bzrkngre/6pyb7Zyb55y73Dm3y9vEscM5x90v72dS4SiWTS/2Oo7EkJvP\nL6e2tYvnR+BZZBU4YVDT0smjG6p5/8JSzVwsb3Pz+eX0OsdvVh/2OookqJd217H9aAt3LptCkhbW\nlD6WTS9iXG46D64deQNdVOCEwb0rD9DT67jzsileR5EYVFaQyRUzxvDQ2ko6u9XZWMLLOcddL+5j\nfF4Gf7NAMxfLWyUn+fjwO8pYuaeOyvp2r+NElQqcYapv6+K3ayq5YV4J5aM1a6gM7OMXTaT+5KkR\nP7OohN+qfSdYf7iRT102mZQkNenydje9o5wkn/HA6kNeR4kq/W8Ypl+sOkinv4fPXD7V6ygSwy6c\nMpppY7L4xSsH6O0dMdOySIT19jr+bfkuSvMz+PA7zjjvoYxwY3PTufa8cfxu7ZERtQCnCpxhqGnp\n5H9fPcj180qYWqxhmXJmZsanLp3CruOtPLdzZC+AJ+Hz+OZqdhxr4ctXT9foTTmrT1w8idYuPw+/\ncWTwjROECpxh+PGKvfT0Or501XSvo0gcuGF+CRNHZ/LT5/dqfSoZtvZTfn7wl93MGZ/De+eWeB1H\nYty8sjzOn1jA/756CP8IGTKuAucc/WTFXh5+o5JFEwpYte+E13Ekhj24ppIH11Ty+3VVLJpQwPaj\nLazoM2Tz9ONazkOG4kfP7eFocyfffO9sfBo5JSH45KWTqW7q4PFNI2N9qpDWopK3cs7x1JajpCT5\nuHx6kddxJI7ML8tj/eEGvv+XXSybXqROoTKogdY621bdzP2vHuIj55ext6aNvTUjcyp+Cd2Dayrp\ndY5xuen83+U7uWF+CckJ3v4k9k8XIc/uqGFvbRtXzhxDdnqK13EkjiT5jK9dO5N9tW38VvPiyDno\n8vfwj49sIT8zha9cM8PrOBJHfGZcMWMM9SdP8ceN1V7HiTgVOEPUfsrPd57cwZicNJZOHu11HIlD\nV80aw0VTR/OjFXtpPHnK6zgSZ37wl93sONbCv79/LnmZmlhUhmbmuGxK8tL56Qt7E34RYBU4Q/Qf\nf9lNdVMH188brxlD5ZyYGd+4bjYnu/x8+8ntXseROLLreAu/WHWQWy+YwJWzxngdR+KQmXH17LEc\naejgf1895HWciFKBMwSrD9Tzy9cOcduFE5lUOMrrOBLHpo/N5rOXT+VPm46y42iL13EkDtS0dPLw\nG0eYXZLD166d6XUciWMVxdlcMaOYu17YR11rl9dxIkYFToia27v5hz9sZsLoTP7xGg0Ll+H77OVT\nmTkuhz9tqh5Rk2/J0LV1+Xlg9WFSknzce+ti0lM0540Mzz+/ZyZd/h7+7emdXkeJGBU4IXDO8eVH\nNnO8uZMffXg+makafCbDl5rs48cfnk+Xv4eH1h6hRzMcywA6TvXwv68epLWzm79dUk5JXobXkSQB\nTC7K4o5LJ/PYhmpe3J2YK42rwAnBva8c4NkdNfzTu2ewsDzf6ziSQKaPzeZ988dzqP4kT2/TOlXy\nVs3t3fzytYPUtnTx0SUTKB+tS+MSPl+4ooKK4iy+9thWWhLwLLIKnEE8s/04//b0Lq49byy3XzzJ\n6ziSgBaU53PhlNG8tr+ee1ce8DqOxIjalk4+fM/rHG3u5CPnlzFtTLbXkSTBpCUn8YMPzqOmpZOv\nPro14WZY17WWs1h/uJEv/m4jc0vz+OGH5vPQ2jOv4aFZaCVUA/2uXHveOFo6/Xxv+U5yM1L4kBZO\nHNG2VTdzx6/X0dTRzccumKi17iRi5pfl8Y/XzODfn97Folfz+bsEOpBXgXMGm4408bH71zI2J51f\nqFOfRJjPjA8tKqVgVCr/+OgW2k/5ue2ixGloJDTOOb78hy08vrmazNRk/u6iSepzI+dkKAfdn7p0\nMhsON/J/l+9kctEolk0vjmCy6NElqgG8tv8Et/xiDQWjUnnojqUUZad5HUlGgOQkH/feuoirZ4/h\nW0/u4LtP7Rgxi+IJNJw8xd8/vIlHNlRRmp/JZ5ZNUXEjUWFm/NeH5jF9bDZ3/mYD6w83eh0pLFTg\n9PPo+qrAmZvcdH53x1LG5aqBkehJS07if25eyG0XTuQXqw5y2/++QW1Lp9exJIJ6ex0Pv1HJO//r\nJf685RhXzhzD7RdP0jIwElXZ6Sn88uPnMyYnjdvuX8vqA/VeRxo2FThBnd09fPWxLXzpD5tZNCGf\nRz59oY6exBPJST6+df1svv+B83jjUANX/3glT205mnAdAAW2VjXzof/3Ol95dCvTirP58xcu4Z0z\nivGZZkmX6CvKTuPBTy6lOCeNW+9fy5Ob43vVcRU4wKv7TnD1j1fy0NojfGbZFH5z+xJyM3X0JN76\n8DvK+fMXLqE0P5PPPbiRW+5by67jmvU4Eaw/3Mht/7uW9961iv11bfzgxrk8/KmlTB+rkVLirZK8\nDB759IWcNz6Xzz+0ka//aSsdp+JzzaoR3cl457EW/uvZ3azYWcvE0Zk8+MklXDil0OtYIm+aWpzF\nHz9zIb9dU8l/Pruba378Cu+ZO447LpnMvLI8r+PJEHR29/DM9uP8bu0RXj9QT8GoVL589XRuvWCC\nLkdJTMkflcrv7ljKD57ZzT0rD/DS7jr+5bpZvGvWGCyOzi6OuAKns7uHF3fV8ts1lazad4Ls9GS+\nfPV0br94kkZKSUxKTvLxsQsncsP8En7xykF++doh/rzlGPNKc7lubgnvPm8spfmZXseUAXR297Du\nUCNPbzvGE5uP0trpZ3xeBv987Uw+urRcs6JLzEpJ8vG1a2fyzhnFfOPxbXzqgfWcNz6XOy6dzFWz\nxsTF38uQ/neZ2TXAT4Ak4BfOuX/v93ga8GtgEVAPfNg5dyi8Uc9Nd08ve2va2FLVxCt7T7ByTx2t\nXX7G5qTz5aun89El5eRlpnodU2RQeZmp/MPV0/nUZZP5w7oqHttYxfeW7+R7y3cyrzSXC6YUsqA8\njwXleRRnp3sd95zEc1sD0HjyFDuOtbCtupnX9tez5mA9nd29pKf4uHbOOG5cVMrSyaPx+eLnKFhG\ntqWTR/PnL1zCYxuq+PlL+/n8QxvJzUjhqlljWDa9iAXl+ZTkpsfkmZ1BCxwzSwL+B7gKqALeMLMn\nnHM7+mx2O9DonJtqZjcB3wc+HK6Qzjn8vQ5/j6O7t5ee4L/+HkeXv5fWzm5aO/20dHRT29rFseZO\njjd3cODESXYdb+WUPzDUtjg7jXefN5b3zivhgsmjSU5SFySJP9npKfzdxZP4u4sncbj+JMu3HufZ\nHce5b9UBunsCHZGLs9OYMDqTsoJMygsyKcxKo2BUKvmZqWSnJ5OekkRaso/0lCTSU3ykJPlI8hlJ\nZpjhSWPldVvjnKPXgb+3l57eQJvT0xNse3p7aev009Lpp7Wz+81/a1q6qG7s4GhTB4frT3K0+a8j\n3iYXjeKmd5Rz6bRClk4erbM1ErdSknx8+B3l3LiojFf3neCxDVU8u/04j6yvAiAvM4XZJTlMKcpi\nTE46Y3LSKc5OY1RaMhkpSWSmBr4yUpNISfLhMyPJZ/gi3NaE8j/ufGCfc+4AgJn9DrgB6Nvo3AB8\nK3j7EeAuMzMXhmEf//GXXfzspf1Dek5qko+xuemUF2Ty8QsnMqskhznjc5lcOComq0yRczVh9Cju\nXDaFO5dNobO7h+1Hm9lwuIndNa1UNrTz2r56HmupHvJ+v3LNDO5cNiUCic/Ks7bmRFsXi7+7YsjP\n8xmMyUmnJC+D8ycVMHNcDrNKcpg5LofCLM2fJYklyWdcOq2IS6cV4e/pZWt1M9uqm9l+tIXtR1v4\n08ZqWjr9Ie+vMCuVdV+/KmJ5QylwxgN91yioApacaRvnnN/MmoHRwIm+G5nZHcAdwbttZrb7XEKH\nYu/A3y7snynGKN/weZ7xo2d/+Kz5BnlutBR+5vuc+Ezo208I0+vGW1tTCJw4GIEdn80wfkc8/78x\nBPGUFeIbqF23AAANyElEQVQw70djIO9hwP5l0M1Ov7dDbmdCKXAGOuXR/2gplG1wzt0D3BPCa0aE\nma1zzi326vUHo3zDF+sZYz0feJoxrtqaePgs+4qnvPGUFZQ3koaTNZROKFVA35X/SoH+s/+8uY2Z\nJQO5QMO5BBKREUttjYiETSgFzhtAhZlNMrNU4CbgiX7bPAF8LHj7RuCFcPS/EZERRW2NiITNoJeo\ngte5Pwc8Q2Do5v3Oue1m9h1gnXPuCeA+4AEz20fgaOqmSIYeBs8uj4VI+YYv1jPGej7wKGMctjXx\n8Fn2FU954ykrKG8knXNW08GPiIiIJBpNBCMiIiIJRwWOiIiIJJyEK3DM7Boz221m+8zsnwZ4PM3M\nHg4+vsbMJsZgxv9jZjvMbIuZPW9m4ZpnJCz5+mx3o5k5M4vqcMNQ8pnZh4Lv4XYzezCa+ULJaGbl\nZvaimW0Mfs7XRjnf/WZWa2bbzvC4mdlPg/m3mNnCaOaLJfHQpvTJEtNtywB5YrqtGSBHzLc9fXLE\ndBs0QJ7wt0nOuYT5ItAxcT8wGUgFNgOz+m3zGeDu4O2bgIdjMOPlQGbw9p3RzBhKvuB22cBKYDWw\nOJbyARXARiA/eL84Bj/je4A7g7dnAYeinPFSYCGw7QyPXws8TWDemaXAmmjmi5WveGhThpjVs7bl\nXPIGt/OkrTnH99fTtmeIWT1tgwbIHPY2KdHO4Lw51btz7hRweqr3vm4AfhW8/QhwhVlU128YNKNz\n7kXnXHvw7moC84HETL6gfwX+A+gc4LFICiXfJ4H/cc41AjjnamMwowNygrdzeft8LxHlnFvJ2eeP\nuQH4tQtYDeSZ2bjopIsp8dCmnBbrbUt/sd7W9BcPbc9pMd8G9ReJNinRCpyBpnoff6ZtnHN+4PRU\n79ESSsa+bidQtUbLoPnMbAFQ5px7Koq5Tgvl/ZsGTDOzV81stQVWqI6mUDJ+C/hbM6sClgOfj060\nkA319zRRxUOb8rYcQbHWtvQX621Nf/HQ9pyWCG1Qf0NukxJteduwTfUeQSG/vpn9LbAYuCyiifq9\n7ADfezOfmfmAHwG3RStQP6G8f8kEThUvI3CE+oqZzXHONUU422mhZPwI8Evn3H+Z2QUE5naZ45zr\njXy8kHj9/yRWxEObclqsty1vizHA92KprekvHtqe0xKhDepvyP/PEu0MTjxM9R5KRszsSuCfgeud\nc11RygaD58sG5gAvmdkhAtdCn4hi579QP+PHnXPdzrmDwG4CjU60hJLxduD3AM6514F0AovKxYqQ\nfk9HgHhoU96WIyjW2pb+Yr2t6S8e2p6+OeK9Depv6G2Sl52KItBJKRk4AEzirx2rZvfb5rO8tUPg\n72Mw4wICHcQqYvE97Lf9S0S3k3Eo7981wK+CtwsJnNYcHWMZnwZuC96eGfyPalH+rCdy5g597+Gt\nHfrWRvt3MRa+4qFNGWJWz9qWc8nbb/uotjXn+P562vYMMavnbdAAucPaJnn2g0TwDboW2BP8T/zP\nwe99h8DRCgSq1D8A+4C1wOQYzLgCqAE2Bb+eiKV8/baNeqMTwvtnwA+BHcBW4KYY/IxnAa8GG55N\nwLuinO8h4BjQTeDI6Hbg08Cn+7yH/xPMv9XLPyxef8VDmzKErJ62LUPN22/bqLc15/D+et72DCGr\np23QAHnD3iZpqQYRERFJOInWB0dEREREBY6IiIgkHhU4IiIiknBU4IiIiEjCUYEjIiIiCSdhCxwz\nu9vM/iVM+yo3szYzSwref8nMPhGOfQf397SZfSxc+xvC637XzE6Y2fEzPH6nmdUEf/ZznnrezJYF\npwM/fX+7mS071/3Fi+D7NtnrHBI5amdCel21MxGkduYsvBz3Pozx8oeADqAVaAJeIzBe3neO+7py\niM95CfjEOWb/FvCbGHgPy4Lv4YCr3QIpwcfnheG1lgFVUfiZbgNWef3ehuHnmAY8DtQRmBH3GWB6\nv23+P+A4gXWP7gfS+jz2rwTmifAD3+r3vHHAEwQm9XLARK9/3lj9UjsTlvdQ7UyMfkW4nXkPsCr4\n/+Y4cC+QHe2fMZ7P4LzXOZcNTAD+HfgKcF+4XyQ49XoimgDUuzOvdjuGwARm26MXSYLyCBQh0wl8\nDmsJNEQAmNnVwD8BVxCY+XMy8O0+z98H/CPw5wH23Qv8BfhABHInIrUzw6N2JnZFsp3JBb4LlBCY\nJbkU+EG4f4BBeV1FnmPleYh+R0MElofvBeYE7/8S+G7wdiHwFIFqsgF4hcDluQeCz+kA2gh8WBMJ\nHNneDlQCK/t8Lzm4v5eAfyPwC9FM4JeiIPjYMvodRZzOS2Aa71MEZmpsAzb32d8ngrd9wNeBw0At\n8GsgN/jY6RwfC2Y7QXCGyjO8T7nB59cF9/f14P6vDP7MvcEcvxygsj8ZfK024IXg92cAzwXfw93A\nh/o8Jw34z2CuGuBuIGOg96Tv50fgSPP3wZytBBq6xX22XQhsDD72B+Dh059rv8wzgU6gJ5i5KdRc\nwc+9lsAsmu/jrzOANsD/397ZxthVVWH4eWWqTR1qWyiSFqlClUAJaEhDfyhEGzXGIqEhArFFCvEH\nxBKhJgpirT8KghEjIjGiQqSWr4IoxhCC35ACCU0IEgwJXyEMbdIqMtQGO/D6Y+3TOXN6Z+bcznRq\nr+tJbuaeu89Ze51z935n77X2uYcra3WsAzYVHwaBLYwx8yzXb2GtPf6IEINB4DHg2JbtfU6xdVjZ\n3ghcXStfCmztcNwGGjOrWlkfGcFJnUmdSZ0ZtjPpOlPbZznw1FT34YM5gjMC248TjehjHYrXlLK5\nxEj1yjjEK4kGeYbtftvX1Y45nWjMnx6lyvOBC4kR6hBwQwsfHwCuBu4s9Z3cYbcLyuvjxIi5H7ix\nsc9HiVH3UmCtpONHqfKHhPgcU87nfGCV7YeAzwADxY8LGn4+Cywqm7Nsf0LSuwnR2QgcQTyJ9iZJ\n1X7XEoL1YWAh8Rj7tWNdjxqfA+5geEZxI4CkdwK/IjrtHOKnvM/qZMD2M0T6YHM5p1kt/TqSmEFW\nn98MrABOIdrS2kZ++0xCAOeUa3GfpGktz/M8YgY0m5j9rG953GmEsOwo24uIn1eveBJ470TWLyTt\nSJ3pSOpM6kwb21MepeuZAU5hgGgQTXYTaw8WOJ7y+leXYeUYrLO90/auUcpvs/032zuBbwKfrxYH\nTpAvANfbft72G8AVwLmNEPa3be+y/STR6PYSsOLLOcAVtgdtvwh8D1i5j34tA160fYvtIdtbgHuA\nsyUJ+BJwme1/2B4kBPbclrYftv07228Rs93qfJYQkYYbyvd2LzGbbUVLv3YD623vJsTvcOAH5Zo9\nTXTKk2r7P2F7U9n/ekK0lrR06V7bj9seAn5JiOF453AUMSO7vPZxPzGjr6jeH9rSj2RipM4UUmdS\nZ1rY/iQRDWw7EJ00ei3vO58I9zX5LhH2ezDaIj+x/Z1xbL3cRflLxGK5yXjU/Lxir267j5gRVtTv\nRvg30RCbHE48RbZpa/4++rUAOFXSa7XP+gihmAvMAJ4o1xfiwWhthbh5PtOL0M4DXmn8kxjve6nT\nxq8dRfAgwukQIWZqn9Wv7576bb9d7tqY19KfNt/bHiTNBR4EbrJ9e63oDWBmbbt6P9jSj2RipM4M\nkzqTOjOW7SVEBOrsErGbUnomgiNpMdGpHm6WlVHyGtvHAGcAl0taWhWPYnK8mdf7au+PJkbo24mc\n8oyaX4cQHaCt3QGik9dtDzGyM7Rhe/GpaeuVLu1UvAz82fas2qvf9sWlrl3AolrZe2yP2bFa8Cow\nXzXVYOR1b9K8tvvDrz31S3oHsXhuYAL2OiJpNiE6v7HdDDE/zcjZ9MnAtlpoOdlPpM7sRepM6sxo\ntj9CpAIvtP37yfC3Ww76AY6kmZKWEWG/Dbaf6rDPMkkLSwN+nVggVo2mtxG5425ZIekESTOIR9Bv\nKiP0Z4mZwWdLzvQqYgFaxTbg/aXRduJ24DJJH5DUz3Aufagb54ovdwHrJR0qaQERftzQjZ0avwU+\nJGmlpGnltVjS8bbfJnLK35d0BICk+WUV/kTYTHxPX5bUJ+lMYpHnaGwDjio5dfaTX6dIWl5mfl8B\n3gQenYC9vZA0k7hl8xHbX++wyy+Ai0r7m020sVtrx0+TNJ3o332SptfTGqWsapPvKtvJGKTOdCZ1\nJnWGDjoj6UTibs3Vtu+fTL+74WAe4NwvaZAY8X+DyFOuGmXfDwIPESG3zUQo7k+l7BrgKkmvSfpq\nF/XfRnzZW4n86KUAtv8FXAL8lJjF7CQWHlbcXf7ukLSlg92fF9t/AV4gVuyv7sKvOqtL/c8TM86N\nxX7XlLzyp4i88gBx3tcyLKpfIxa0PSrpdeJ6H7ePfld1/odYfX8RcWfKCkIA3xzlkD8Qs46tkrbv\nJ79+Taw5+CexzmB5yZNPJmcBi4FVih/xql5Hw55FpNcBfyTSAS8B36odfzMxozyP6Bu7GLkmorqb\nB+DvDIfMk71JnRmf1JnUmabOrCEiij+r2Z3yRcYafw1ckvzvIOkx4Me2bzkAda8jbsdcMdV1J0ky\ndaTO9AYHcwQn+T9A0umSjiyh4y8Sdxo8cKD9SpKkd0id6U167S6qpPc4jsjx9wPPEavxXz2wLiVJ\n0mOkzvQgmaJKkiRJkqTnyBRVkiRJkiQ9Rw5wkiRJkiTpOXKAkyRJkiRJz5EDnCRJkiRJeo4c4CRJ\nkiRJ0nP8F8jbaDV/ENC9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe09d208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#temperature and feeling temperature\n",
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "plt.subplot(221)\n",
    "sns.distplot(data_2011.temp.values, bins=40,kde=True)\n",
    "plt.xlabel('Distribution of temperature in 2011', fontsize=12)\n",
    "plt.subplot(222)\n",
    "sns.distplot(data_2012.temp.values, bins=40,kde=True)\n",
    "plt.xlabel('Distribution of temperature in 2012', fontsize=12)\n",
    "\n",
    "plt.subplot(223)\n",
    "sns.distplot(data_2011.atemp.values, bins=40,kde=True)\n",
    "plt.xlabel('Distribution of feeling temp in 2011', fontsize=12)\n",
    "plt.subplot(224)\n",
    "sns.distplot(data_2012.atemp.values, bins=40,kde=True)\n",
    "plt.xlabel('Distribution of feeling temp in 2012', fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "#temp 和 atemp的影响也相似"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LxujkQDWtEeBKY8xKYBVwvYhstDhTSHqtqp3clFgW5ERP25OfGk9+ahx79CBf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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcdcea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# windspeed\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.distplot(data[data.yr==0].windspeed.values, bins=40,kde=True)\n",
    "plt.xlabel('Distribution of windspeed in 2011', fontsize=12)\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.distplot(data[data.yr==1].windspeed.values, bins=40,kde=True)\n",
    "plt.xlabel('Distribution of windspeed in 2012', fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BLvTfcSriPLe/normHn5220pKMqd/VJiXGkduSiy7tQOhUj7V1T/EkbpO1pYGdvVwf3HY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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xf2454e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of humidity\n",
    "plt.figure(figsize=(8,4))\n",
    "\n",
    "plt.subplot(121)\n",
    "sns.distplot(data_2011.hum.values, bins=40,kde=True)\n",
    "plt.xlabel('Distribution of humidity in 2011', fontsize=12)\n",
    "\n",
    "plt.subplot(122)\n",
    "sns.distplot(data_2012.hum.values, bins=40,kde=True)\n",
    "plt.xlabel('Distribution of humidity in 2012', fontsize=12)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#get the names of all the columns\n",
    "cols=data.columns \n",
    "\n",
    "# Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_corr = data.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 12)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ngBHA44lhVZRSe5VSp4EPgMopy/cBvkqpDwF9yrI6wDIATdPOAVeA8inrdmiaFqdp2gMg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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe2dc4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)\n",
    "\n",
    "# Mask unimportant fetures\n",
    "sns.heatmap(data_corr, mask=data_corr < 1, cbar=False)\n",
    "\n",
    "plt.savefig('Bike_Corr.png' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp and atemp = 0.99\n",
      "season and mnth = 0.83\n",
      "atemp and cnt = 0.63\n",
      "temp and cnt = 0.63\n",
      "weathersit and hum = 0.59\n",
      "yr and cnt = 0.57\n"
     ]
    }
   ],
   "source": [
    "#Set the threshold to select only highly correlated attributes\n",
    "threshold = 0.5\n",
    "# List of pairs along with correlation above threshold\n",
    "corr_list = []\n",
    "#size = data.shape[1]\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "#Search for the highly correlated pairs\n",
    "for i in range(0, size): #for 'size' features\n",
    "    for j in range(i+1,size): #avoid repetition\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1) or (data_corr.iloc[i,j] < 0 and data_corr.iloc[i,j] <= -threshold):\n",
    "            corr_list.append([data_corr.iloc[i,j],i,j]) #store correlation and columns index\n",
    "\n",
    "#Sort to show higher ones first            \n",
    "s_corr_list = sorted(corr_list,key=lambda x: -abs(x[0]))\n",
    "\n",
    "#Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print (\"%s and %s = %.2f\" % (cols[i],cols[j],v))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#根据数据探索，考虑单车使用次数是在不断发展的，因此年份信息需要使用\n",
    "#但是年份信息在训练集内是不会更改的，因此考虑将月份特征和年份特征合并\n",
    "#mnth_new = mnth + yr * 12\n",
    "data_2011['mnth'] = data_2011['mnth'].values + 12 * data_2011['yr'].values\n",
    "data_2012['mnth'] = data_2012['mnth'].values + 12 * data_2012['yr'].values\n",
    "\n",
    "#mnth + yr   The r2 score of RidgeCV on test is 0.444299136053\n",
    "#mnth        The r2 score of RidgeCV on test is -0.797358609406\n",
    "\n",
    "#Origin Count  The r2 score of RidgeCV on test is -0.157461901045\n",
    "#log(count)    The r2 score of RidgeCV on test is 0.444299136053\n",
    "\n",
    "###################Count 处理###########################################\n",
    "#Count取Log\n",
    "data_2011['cnt']= np.log10(data_2011['cnt'].values)\n",
    "data_2012['cnt']= np.log10(data_2012['cnt'].values)\n",
    "\n",
    "#去除噪声值 log(cnt)<noise_level\n",
    "#测试集不做噪声处理 The r2 score of RidgeCV on test is 0.398959513722\n",
    "\n",
    "noise_level = 2.9\n",
    "data_2011 = data_2011[data_2011.cnt > noise_level]\n",
    "\n",
    "#测试集去噪声\n",
    "#测试集 去噪声后  he r2 score of RidgeCV on test is 0.439382107076\n",
    "data_2012 = data_2012[data_2012.cnt > noise_level]\n",
    "########################################################################\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#从2011年和2012年数据中分离测试集和训练集的输入特征x和输出y\n",
    "y_train = data_2011['cnt'].values\n",
    "X_train_org = data_2011.drop(['cnt','yr'],axis=1)\n",
    "\n",
    "y_test = data_2012['cnt'].values\n",
    "X_test_org = data_2012.drop(['cnt','yr'], axis=1)\n",
    "\n",
    "#用于后续显示权重系数对应的特征\n",
    "# columns = X_train_used.columns\n",
    "# print(columns)\n",
    "# X_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import Normalizer\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import MaxAbsScaler\n",
    "# 分别初始化对特征和目标值的标准化器\n",
    "\n",
    "# ss_X = Normalizer()  #The r2 score of LassoCV on test is -0.837689332475\n",
    "\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "# ss_X = MinMaxScaler()\n",
    "#ss_y = MinMaxScaler()\n",
    "\n",
    "# ss_X = MaxAbsScaler()\n",
    "# ss_y = MaxAbsScaler()\n",
    "\n",
    "\n",
    "\n",
    "#仅对temp,atemp,hum,windspeed进行标准化, mnth也被作为连续值处理\n",
    "# 分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "#\"mnth\",\"atemp\",\"hum\",\"windspeed\"，\"temp\"\n",
    "\n",
    "X_train_ss = ss_X.fit_transform(X_train_org[[\"mnth\",\"temp\",\"hum\",\"windspeed\" ]])\n",
    "X_test_ss = ss_X.transform(X_test_org[[\"mnth\",\"temp\",\"hum\",\"windspeed\" ]])\n",
    "\n",
    "\n",
    "\n",
    "#\"mnth\",\"temp\",\"hum\",\"windspeed\"  The r2 score of RidgeCV on test is 0.444299136053\n",
    "#\"mnth\",\"atemp\",\"temp\",\"hum\",\"windspeed\" The r2 score of RidgeCV on test is 0.439382107076\n",
    "#\"mnth\",\"atemp\",\"hum\",\"windspeed\" The r2 score of RidgeCV on test is 0.426797124918\n",
    "#\"mnth\",\"atemp\",\"windspeed\" The r2 score of RidgeCV on test is 0.344462443474\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-1.62828436 -0.77803595  1.15005807 -0.39154932]\n",
      " [-1.62828436 -0.67573456  0.38165172  0.76463491]\n",
      " [-1.62828436 -1.56103282 -1.43048104  0.76161626]\n",
      " ..., \n",
      " [ 1.59788753 -1.28572336 -0.47199308 -0.93010361]\n",
      " [ 1.59788753 -0.950207   -0.03438809 -0.73421916]\n",
      " [ 1.59788753 -0.42928096 -0.18026109  0.39209351]]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([u'season', u'yr', u'mnth', u'holiday', u'weekday', u'workingday',\n",
      "       u'weathersit', u'temp', u'atemp', u'hum', u'windspeed', u'cnt'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#利用Onehot Encode编码season,mnth,holiday,weekday,workingday,weathersit\n",
    "print(data_2011.columns)\n",
    "enc = OneHotEncoder()\n",
    "# enc.fit(X_train_org[['season', 'holiday', 'weekday', 'workingday',\n",
    "#        'weathersit']])\n",
    "\n",
    "X_train_enc=enc.fit_transform(X_train_org[['season', 'holiday', 'weekday', 'workingday',\n",
    "       'weathersit']]).toarray()\n",
    "X_test_enc=enc.transform(X_test_org[['season', 'holiday', 'weekday', 'workingday',\n",
    "       'weathersit']]).toarray()\n",
    "# print(enc.n_values_)\n",
    "# print(enc.feature_indices_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(364L, 18L)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_used = np.hstack([X_train_enc, X_train_ss])\n",
    "X_test_used = np.hstack([X_test_enc, X_test_ss])\n",
    "X_test_enc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "# from sklearn.preprocessing import MaxAbsScaler\n",
    "# mas_y = MaxAbsScaler()\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1,1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1,1))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 确定模型类型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 尝试缺省参数的线性回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 线性回归\n",
    "#class sklearn.linear_model.LinearRegression(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 使用默认配置初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train_used, y_train)\n",
    "\n",
    "# # 预测\n",
    "y_test_pred_lr = lr.predict(X_test_used)\n",
    "y_train_pred_lr = lr.predict(X_train_used)\n",
    "\n",
    "\n",
    "# # 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "# fs = pd.DataFrame({\"columns\":list(columns), \"coef\":list((lr.coef_.T))})\n",
    "# fs.sort_values(by=['coef'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1.1 模型评价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is 0.42636330226\n",
      "The r2 score of LinearRegression on train is 0.815259431177\n"
     ]
    }
   ],
   "source": [
    "# 使用r2_score评价模型在测试集和训练集上的性能，并输出评估结果\n",
    "#测试集\n",
    "print 'The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr)\n",
    "#训练集\n",
    "print 'The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rmOdE4PSI+AtgOHAgtZ71qIgYWvWmJwKP9V6ZkiQ1n2570pl5aWZOzMxJwHuAH2XmWcCd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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe4be2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#在训练集上观察预测残差的分布，看是否符合模型假设：噪声为0均值的高斯噪声\n",
    "f, ax = plt.subplots(figsize=(7, 5)) \n",
    "f.tight_layout() \n",
    "ax.hist(y_train - y_train_pred_lr,bins=40, label='Residuals Linear', color='b', alpha=.5); \n",
    "ax.set_title(\"Histogram of Residuals\") \n",
    "ax.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dFjcpiSaYjO61XFeL2d0TicXfJNcIpZEUrTcI3vbdGtg2BVvGdsWMtDYAgC+2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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdeb5b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.subplot(121)\n",
    "plt.scatter(y_test, y_test_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.title('Test')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.subplot(122)\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.title('Train')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.444299136053\n",
      "The r2 score of RidgeCV on train is 0.815065703363\n"
     ]
    }
   ],
   "source": [
    "#岭回归\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train_used, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test_used)\n",
    "y_train_pred_ridge = ridge.predict(X_train_used)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BI4ShYV3ymHxRR/JysvwWMRqqP8dhMN1Ufngvu94ai2vfDuqfel5QMbi1Zs7qAtkqLoQI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xde21fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.subplot(121)\n",
    "plt.scatter(y_test, y_test_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.title('Test')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.subplot(122)\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.title('Test')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xdcf8c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 1.0)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>[0.561834983215]</td>\n",
       "      <td>[0.564058226832]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>[0.480208020139]</td>\n",
       "      <td>[0.448375305743]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.301820012212]</td>\n",
       "      <td>[0.290333751977]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>[0.279529518799]</td>\n",
       "      <td>[0.255746931257]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[0.273246979508]</td>\n",
       "      <td>[0.274163236548]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>[0.187916941061]</td>\n",
       "      <td>[0.193274044034]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.163022032774]</td>\n",
       "      <td>[0.157413747618]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>[0.0797740465221]</td>\n",
       "      <td>[0.0774718290473]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[0.0450983235449]</td>\n",
       "      <td>[0.0449545058532]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>[0.0381496627071]</td>\n",
       "      <td>[0.0349874127173]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[0.0116776593347]</td>\n",
       "      <td>[0.013884202127]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[0.00154217066092]</td>\n",
       "      <td>[0.00173340160156]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-0.026073976295]</td>\n",
       "      <td>[-0.0298422538134]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[-0.0275982141817]</td>\n",
       "      <td>[-0.0273568869918]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.0330559144111]</td>\n",
       "      <td>[-0.0340919913065]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[-0.0358136876547]</td>\n",
       "      <td>[-0.0341106440008]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>[-0.0797740465221]</td>\n",
       "      <td>[-0.0774718290473]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>[-0.0940397622219]</td>\n",
       "      <td>[-0.100428966069]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>[-0.110107277808]</td>\n",
       "      <td>[-0.112754315152]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[-0.163022032774]</td>\n",
       "      <td>[-0.157413747618]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[-0.548993015426]</td>\n",
       "      <td>[-0.534654734712]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>[-0.759737538937]</td>\n",
       "      <td>[-0.704122236999]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               coef_lr          coef_ridge\n",
       "19    [0.561834983215]    [0.564058226832]\n",
       "15    [0.480208020139]    [0.448375305743]\n",
       "3     [0.301820012212]    [0.290333751977]\n",
       "16    [0.279529518799]    [0.255746931257]\n",
       "1     [0.273246979508]    [0.274163236548]\n",
       "18    [0.187916941061]    [0.193274044034]\n",
       "4     [0.163022032774]    [0.157413747618]\n",
       "14   [0.0797740465221]   [0.0774718290473]\n",
       "6    [0.0450983235449]   [0.0449545058532]\n",
       "12   [0.0381496627071]   [0.0349874127173]\n",
       "11   [0.0116776593347]    [0.013884202127]\n",
       "10  [0.00154217066092]  [0.00173340160156]\n",
       "2    [-0.026073976295]  [-0.0298422538134]\n",
       "8   [-0.0275982141817]  [-0.0273568869918]\n",
       "9   [-0.0330559144111]  [-0.0340919913065]\n",
       "7   [-0.0358136876547]  [-0.0341106440008]\n",
       "13  [-0.0797740465221]  [-0.0774718290473]\n",
       "20  [-0.0940397622219]   [-0.100428966069]\n",
       "21   [-0.110107277808]   [-0.112754315152]\n",
       "5    [-0.163022032774]   [-0.157413747618]\n",
       "0    [-0.548993015426]   [-0.534654734712]\n",
       "17   [-0.759737538937]   [-0.704122236999]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({ \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.437602014425\n",
      "The r2 score of LassoCV on train is 0.814025782175\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda2\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "#lasso = LassoCV(alphas=alphas)  \n",
    "lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train_used, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test_used)\n",
    "y_train_pred_lasso = lasso.predict(X_train_used)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso)\n",
    "print 'The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BI4ShYV3ymHxRR/JysvwWMRqqP8dhMN1Ufngvu94ai2vfDuqfel5QMbi1Zs7qAtkqLoQI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xe76a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#还可以观察预测值与真值的散点图\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.subplot(121)\n",
    "plt.scatter(y_test, y_test_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.title('Test')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.subplot(122)\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')   #数据已经标准化，3倍标准差即可\n",
    "plt.title('Test')\n",
    "plt.xlabel('True price')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xe7594a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.003270090490288099)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.523680e-01</td>\n",
       "      <td>[0.561834983215]</td>\n",
       "      <td>[0.564058226832]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.742995e-01</td>\n",
       "      <td>[0.163022032774]</td>\n",
       "      <td>[0.157413747618]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1.923117e-01</td>\n",
       "      <td>[0.187916941061]</td>\n",
       "      <td>[0.193274044034]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1.899079e-01</td>\n",
       "      <td>[0.480208020139]</td>\n",
       "      <td>[0.448375305743]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.095811e-01</td>\n",
       "      <td>[0.301820012212]</td>\n",
       "      <td>[0.290333751977]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.002207e-01</td>\n",
       "      <td>[0.273246979508]</td>\n",
       "      <td>[0.274163236548]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.123557e-02</td>\n",
       "      <td>[0.0116776593347]</td>\n",
       "      <td>[0.013884202127]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.466615e-17</td>\n",
       "      <td>[0.0797740465221]</td>\n",
       "      <td>[0.0774718290473]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-0.0275982141817]</td>\n",
       "      <td>[-0.0273568869918]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[0.0450983235449]</td>\n",
       "      <td>[0.0449545058532]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>[0.00154217066092]</td>\n",
       "      <td>[0.00173340160156]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-0.163022032774]</td>\n",
       "      <td>[-0.157413747618]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[0.0381496627071]</td>\n",
       "      <td>[0.0349874127173]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[0.279529518799]</td>\n",
       "      <td>[0.255746931257]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>[-0.0330559144111]</td>\n",
       "      <td>[-0.0340919913065]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-2.366986e-03</td>\n",
       "      <td>[-0.0358136876547]</td>\n",
       "      <td>[-0.0341106440008]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-9.098993e-02</td>\n",
       "      <td>[-0.0797740465221]</td>\n",
       "      <td>[-0.0774718290473]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-9.718969e-02</td>\n",
       "      <td>[-0.0940397622219]</td>\n",
       "      <td>[-0.100428966069]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-1.095093e-01</td>\n",
       "      <td>[-0.110107277808]</td>\n",
       "      <td>[-0.112754315152]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.691016e-01</td>\n",
       "      <td>[-0.026073976295]</td>\n",
       "      <td>[-0.0298422538134]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-7.068432e-01</td>\n",
       "      <td>[-0.548993015426]</td>\n",
       "      <td>[-0.534654734712]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-9.256685e-01</td>\n",
       "      <td>[-0.759737538937]</td>\n",
       "      <td>[-0.704122236999]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      coef_lasso             coef_lr          coef_ridge\n",
       "19  5.523680e-01    [0.561834983215]    [0.564058226832]\n",
       "4   2.742995e-01    [0.163022032774]    [0.157413747618]\n",
       "18  1.923117e-01    [0.187916941061]    [0.193274044034]\n",
       "15  1.899079e-01    [0.480208020139]    [0.448375305743]\n",
       "3   1.095811e-01    [0.301820012212]    [0.290333751977]\n",
       "1   1.002207e-01    [0.273246979508]    [0.274163236548]\n",
       "11  1.123557e-02   [0.0116776593347]    [0.013884202127]\n",
       "14  3.466615e-17   [0.0797740465221]   [0.0774718290473]\n",
       "8  -0.000000e+00  [-0.0275982141817]  [-0.0273568869918]\n",
       "6   0.000000e+00   [0.0450983235449]   [0.0449545058532]\n",
       "10  0.000000e+00  [0.00154217066092]  [0.00173340160156]\n",
       "5  -0.000000e+00   [-0.163022032774]   [-0.157413747618]\n",
       "12 -0.000000e+00   [0.0381496627071]   [0.0349874127173]\n",
       "16 -0.000000e+00    [0.279529518799]    [0.255746931257]\n",
       "9  -0.000000e+00  [-0.0330559144111]  [-0.0340919913065]\n",
       "7  -2.366986e-03  [-0.0358136876547]  [-0.0341106440008]\n",
       "13 -9.098993e-02  [-0.0797740465221]  [-0.0774718290473]\n",
       "20 -9.718969e-02  [-0.0940397622219]   [-0.100428966069]\n",
       "21 -1.095093e-01   [-0.110107277808]   [-0.112754315152]\n",
       "2  -1.691016e-01   [-0.026073976295]  [-0.0298422538134]\n",
       "0  -7.068432e-01   [-0.548993015426]   [-0.534654734712]\n",
       "17 -9.256685e-01   [-0.759737538937]   [-0.704122236999]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lasso'],ascending=False)"
   ]
  }
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